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: "deep reinforcement learning for online computation offloading in wireless powered mobile edge computing networks"
  Abstract: Wireless powered mobile-edge computing (MEC) has recently emerged as a promising paradigm to enhance the data processing capability of low-power networks, such as wireless sensor networks and internet of things (IoT). In this paper, we consider a wireless powered MEC network that adopts a binary offloading policy, so that each computation task of wireless devices (WDs) is either executed locally or fully offloaded to an MEC server. Our goal is to acquire an online algorithm that optimally adapts task offloading decisions and wireless resource allocations to the time-varying wireless channel conditions. This requires quickly solving hard combinatorial optimization problems within the channel coherence time, which is hardly achievable with conventional numerical optimization methods. To tackle this problem, we propose a Deep Reinforcement learning-based Online Offloading (DROO) framework that implements a deep neural network as a scalable solution that learns the binary offloading decisions from the experience. It eliminates the need of solving combinatorial optimization problems, and thus greatly reduces the computational complexity especially in large-size networks. To further reduce the complexity, we propose an adaptive procedure that automatically adjusts the parameters of the DROO algorithm on the fly. Numerical results show that the proposed algorithm can achieve near-optimal performance while significantly decreasing the computation time by more than an order of magnitude compared with existing optimization methods. For example, the CPU execution latency of DROO is less than $0.1$ second in a $30$-user network, making real-time and optimal offloading truly viable even in a fast fading environment.

Connected through hyperedge HE-1 (induced by source paper "stacked auto encoder based deep reinforcement learning for online resource scheduling in large scale mec networks"):
  with members:
    - "unified offloading decision making and resource allocation in me ran"
    - "deep reinforcement learning that matters"
    - "a survey on mobile edge computing the communication perspective"
    - "caching in the sky proactive deployment of cache enabled unmanned aerial vehicles for optimized quality of experience"
    - "prioritized experience replay"
    - "deep reinforcement learning with double q learning"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (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"
    - "a survey on mobile edge computing the communication perspective"
    - "joint offloading and computing optimization in wireless powered mobile edge computing systems"
    - "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-3 (induced by source paper "deep reinforcement learning for autonomous internet of things model applications and challenges"):
  with members:
    - "lifelong federated reinforcement learning a learning architecture for navigation in cloud robotic systems"
    - "interference management for cellular connected uavs a deep reinforcement learning approach"
    - "vehicular edge computing via deep reinforcement learning"
    - "leveraging machine learning and big data for smart buildings a comprehensive survey"
    - "exploring applications of deep reinforcement learning for real world autonomous driving systems"
    - "toward intelligent vehicular networks a machine learning framework"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "cooperative deep reinforcement learning for multiple group nb iot networks optimization"

Connected through hyperedge HE-4 (induced by source paper "reusing wireless power transfer for backscatter assisted relaying in wpcns"):
  with members:
    - "backscatter assisted relaying in wireless powered communications network"
    - "joint tx power allocation and rx power splitting for swipt system with multiple nonlinear energy harvesting circuits"
    - "multi antenna enabled cluster based cooperation in wireless powered communication networks"
    - "distributed scheduling in wireless powered communication network protocol design and performance analysis"
    - "ambient backscatter communications a contemporary survey"
    - "user cooperation for enhanced throughput fairness in wireless powered communication networks"
    - "distributed charging control in broadband wireless power transfer networks"
    - "wirelessly powered backscatter communication networks modeling coverage and capacity"

Connected through hyperedge HE-5 (induced by source paper "collaborative computation offloading in wireless powered mobile edge computing systems"):
  with members:
    - "joint computation and communication cooperation for energy efficient mobile edge computing"
    - "wireless powered user cooperative computation in mobile edge computing systems"
    - "joint tx power allocation and rx power splitting for swipt system with multiple nonlinear energy harvesting circuits"
    - "multiuser computation offloading and downloading for edge computing with virtualization"
    - "online learning for offloading and autoscaling in energy harvesting mobile edge computing"
    - "price based distributed offloading for mobile edge computing with computation capacity constraints"
    - "a survey on mobile edge computing the communication perspective"
    - "multi antenna enabled cluster based cooperation in wireless powered communication networks"

Connected through hyperedge HE-6 (induced by source paper "joint optimization of service caching placement and computation offloading in mobile edge computing system"):
  with members:
    - "joint service placement and request routing in multi cell mobile edge computing networks"
    - "joint service caching and task offloading for mobile edge computing in dense networks"
    - "spatio temporal edge service placement a bandit learning approach"
    - "price based distributed offloading for mobile edge computing with computation capacity constraints"
    - "a survey on mobile edge computing the communication perspective"
    - "energy efficient resource allocation for cache assisted mobile edge computing"
    - "joint offloading and computing optimization in wireless powered mobile edge computing systems"
    - "live prefetching for mobile computation offloading"

Connected through hyperedge HE-7 (induced by source paper "optimal offloading and resource allocation in mobile edge computing with inter user task dependency"):
  with members:
    - "a survey on mobile edge computing the communication perspective"
    - "joint offloading and computing optimization in wireless powered mobile edge computing systems"
    - "energy efficient mobile cloud computing powered by wireless energy transfer"
    - "joint optimization of radio resources and code partitioning in mobile cloud computing"

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.NI(Networking and Internet Architecture)