human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "joint offloading and computing optimization in wireless powered mobile edge computing systems"
  Abstract: Mobile-edge computing (MEC) and wireless power transfer (WPT) have been recognized as promising techniques in the Internet of Things (IoT) era to provide massive low-power wireless devices with enhanced computation capability and sustainable energy supply. In this paper, we propose a unified MEC-WPT design by considering a wireless powered multiuser MEC system, where a multi-antenna access point (AP) (integrated with an MEC server) broadcasts wireless power to charge multiple users and each user node relies on the harvested energy to execute computation tasks. With MEC, these users can execute their respective tasks locally by themselves or offload all or part of them to the AP based on a time division multiple access (TDMA) protocol. Building on the proposed model, we develop an innovative framework to improve the MEC performance, by jointly optimizing the energy transmit beamformer at the AP, the central processing unit (CPU) frequencies and the numbers of offloaded bits at the users, as well as the time allocation among users. Under this framework, we address a practical scenario where latency-limited computation is required. In this case, we develop an optimal resource allocation scheme that minimizes the AP's total energy consumption subject to the users' individual computation latency constraints. Leveraging the state-of-the-art optimization techniques, we derive the optimal solution in a semi-closed form. Numerical results demonstrate the merits of the proposed design over alternative benchmark schemes.

Connected through hyperedge HE-1 (induced by source paper "energy efficient federated edge learning with joint communication and computation design"):
  with members:
    - "energy efficient radio resource allocation for federated edge learning"
    - "machine learning at the wireless edge distributed stochastic gradient descent over the air"
    - "energy efficient federated learning over wireless communication networks"
    - "joint communication and computation optimization for wireless powered mobile edge computing with d2d offloading"
    - "an overview of data importance aware radio resource management for edge machine learning"
    - "in edge ai intelligentizing mobile edge computing caching and communication by federated learning"
    - "robust and communication efficient federated learning from non iid data"
    - "edge ai on demand accelerating deep neural network inference via edge computing"

Connected through hyperedge HE-2 (induced by source paper "analysis on computation intensive status update in mobile edge computing"):
  with members:
    - "trading off computation with transmission in status update systems"
    - "age of information for computation intensive messages in mobile edge computing"
    - "timely cloud computing preemption and waiting"
    - "age of information in a network of preemptive servers"
    - "the age of information in networks moments distributions and sampling"
    - "joint status sampling and updating for minimizing age of information in the internet of things"
    - "joint information freshness and completion time optimization for vehicular networks"
    - "a survey on mobile edge computing the communication perspective"

Connected through hyperedge HE-3 (induced by source paper "joint optimization of signal design and resource allocation in wireless d2d edge computing"):
  with members:
    - "joint task assignment and resource allocation for d2d enabled mobile edge computing"
    - "joint computation and communication cooperation for energy efficient mobile edge computing"
    - "decentralized computation offloading for multi user mobile edge computing a deep reinforcement learning approach"
    - "multiuser resource allocation for mobile edge computation offloading"
    - "joint optimization of radio and computational resources for multicell mobile edge computing"
    - "the interplay between massive mimo and underlaid d2d networking"
    - "on feasibility of interference alignment in mimo interference networks"
    - "on feasibility of interference alignment in mimo interference networks"

Connected through hyperedge HE-4 (induced by source paper "offloading and resource allocation with general task graph in mobile edge computing a deep reinforcement learning approach"):
  with members:
    - "optimized computation offloading performance in virtual edge computing systems via deep reinforcement learning"
    - "deep reinforcement learning for online computation offloading in wireless powered mobile edge computing networks"
    - "a survey on mobile edge computing the communication perspective"
    - "asynchronous methods for deep reinforcement learning"
    - "energy efficient resource allocation for mobile edge computation offloading"
    - "energy efficient mobile cloud computing powered by wireless energy transfer"
    - "the energy frequency convexity rule modeling and experimental validation on mobile devices"

Connected through hyperedge HE-5 (induced by source paper "cognitive wireless power transfer in the presence of reactive primary communication user"):
  with members:
    - "fundamentals of wireless information and power transfer from rf energy harvester models to signal and system designs"
    - "large scale multiantenna multisine wireless power transfer"
    - "distributed wireless power transfer with energy feedback"
    - "waveform optimization for radio frequency wireless power transfer"
    - "waveform design for wireless power transfer"
    - "surveillance and intervention of infrastructure free mobile communications a new wireless security paradigm"
    - "a general design framework for mimo wireless energy transfer with limited feedback"
    - "wireless powered communication networks an overview"

Connected through hyperedge HE-6 (induced by source paper "common throughput maximization for uav enabled interference channel with wireless powered communications"):
  with members:
    - "uav communications for 5g and beyond recent advances and future trends"
    - "fundamentals of wireless information and power transfer from rf energy harvester models to signal and system designs"
    - "capacity characterization of uav enabled two user broadcast channel"
    - "cellular enabled uav communication a connectivity constrained trajectory optimization perspective"
    - "optimal 1d trajectory design for uav enabled multiuser wireless power transfer"
    - "minimum throughput maximization in uav aided wireless powered communication networks"
    - "a tutorial on uavs for wireless networks applications challenges and open problems"
    - "uav enabled wireless power transfer with directional antenna a two user case"

Connected through hyperedge HE-7 (induced by source paper "optimal energy allocation and task offloading policy for wireless powered mobile edge computing systems"):
  with members:
    - "joint communication and computation optimization for wireless powered mobile edge computing with d2d offloading"
    - "optimal resource allocation for wireless powered mobile edge computing with dynamic task arrivals"
    - "fundamentals of wireless information and power transfer from rf energy harvester models to signal and system designs"
    - "joint computation and communication cooperation for energy efficient mobile edge computing"
    - "multiuser computation offloading and downloading for edge computing with virtualization"
    - "deep reinforcement learning for online offloading in wireless powered mobile edge computing networks"
    - "online learning for offloading and autoscaling in energy harvesting mobile edge computing"
    - "a survey on mobile edge computing the communication perspective"

Connected through hyperedge HE-8 (induced by source paper "energy efficient resource allocation for secure noma enabled mobile edge computing networks"):
  with members:
    - "securing downlink massive mimo noma networks with artificial noise"
    - "joint power and time allocation for noma mec offloading"
    - "improving anti eavesdropping ability without eavesdropper s csi a practical secure transmission design perspective"
    - "joint computation and communication cooperation for energy efficient mobile edge computing"
    - "exploiting physical layer security for multiuser multicarrier computation offloading"
    - "edge computing aware noma for 5g networks"
    - "impact of non orthogonal multiple access on the offloading of mobile edge computing"
    - "robust and secure resource allocation for full duplex miso multicarrier noma systems"

Question: Please tell me which class the center node belongs to. The 40 classes are: cs.NA(Numerical Analysis), cs.MM(Multimedia), cs.LO(Logic in Computer Science), cs.CY(Computers and Society), cs.CR(Cryptography and Security), cs.DC(Distributed, Parallel, and Cluster Computing), cs.HC(Human-Computer Interaction), cs.CE(Computational Engineering, Finance, and Science), cs.NI(Networking and Internet Architecture), cs.CC(Computational Complexity), cs.AI(Artificial Intelligence), cs.MA(Multiagent Systems), cs.GL(General Literature), cs.NE(Neural and Evolutionary Computing), cs.SC(Symbolic Computation), cs.AR(Hardware Architecture), cs.CV(Computer Vision and Pattern Recognition), cs.GR(Graphics), cs.ET(Emerging Technologies), cs.SY(Systems and Control), cs.CG(Computational Geometry), cs.OH(Other Computer Science), cs.PL(Programming Languages), cs.SE(Software Engineering), cs.LG(Machine Learning), cs.SD(Sound), cs.SI(Social and Information Networks), cs.RO(Robotics), cs.IT(Information Theory), cs.PF(Performance), cs.CL(Computation and Language), cs.IR(Information Retrieval), cs.MS(Mathematical Software), cs.FL(Formal Languages and Automata Theory), cs.DS(Data Structures and Algorithms), cs.OS(Operating Systems), cs.GT(Computer Science and Game Theory), cs.DB(Databases), cs.DL(Digital Libraries), cs.DM(Discrete Mathematics). Directly output the class name.

gpt: cs.IT(Information Theory)