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: "energy efficient resource allocation for mobile edge computation offloading"
  Abstract: Mobile-edge computation offloading (MECO) offloads intensive mobile computation to clouds located at the edges of cellular networks. Thereby, MECO is envisioned as a promising technique for prolonging the battery lives and enhancing the computation capacities of mobiles. In this paper, we study resource allocation for a multiuser MECO system based on time-division multiple access (TDMA) and orthogonal frequency-division multiple access (OFDMA). First, for the TDMA MECO system with infinite or finite computation capacity, the optimal resource allocation is formulated as a convex optimization problem for minimizing the weighted sum mobile energy consumption under the constraint on computation latency. The optimal policy is proved to have a threshold-based structure with respect to a derived offloading priority function, which yields priorities for users according to their channel gains and local computing energy consumption. As a result, users with priorities above and below a given threshold perform complete and minimum offloading, respectively. Moreover, for the cloud with finite capacity, a sub-optimal resource-allocation algorithm is proposed to reduce the computation complexity for computing the threshold. Next, we consider the OFDMA MECO system, for which the optimal resource allocation is formulated as a non-convex mixed-integer problem. To solve this challenging problem and characterize its policy structure, a sub-optimal low-complexity algorithm is proposed by transforming the OFDMA problem to its TDMA counterpart. The corresponding resource allocation is derived by defining an average offloading priority function and shown to have close-to-optimal performance by simulation.

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 "hfel joint edge association and resource allocation for cost efficient hierarchical federated edge learning"):
  with members:
    - "energy efficient radio resource allocation for federated edge learning"
    - "accelerating dnn training in wireless federated edge learning system"
    - "edge intelligence paving the last mile of artificial intelligence with edge computing"
    - "federated learning over wireless networks convergence analysis and resource allocation"
    - "client edge cloud hierarchical federated learning"
    - "edge ai on demand accelerating deep neural network inference via edge computing"
    - "hybrid fl cooperative learning mechanism using non iid data in wireless networks"
    - "client selection for federated learning with heterogeneous resources in mobile edge"

Connected through hyperedge HE-4 (induced by source paper "distributed task replication for vehicular edge computing performance analysis and learning based algorithm"):
  with members:
    - "adaptive learning based task offloading for vehicular edge computing systems"
    - "exploiting moving intelligence delay optimized computation offloading in vehicular fog networks"
    - "heterogeneous coded computation across heterogeneous workers"
    - "hierarchical coded computation"
    - "speeding up distributed machine learning using codes"
    - "dense moving fog for intelligent iot key challenges and opportunities"
    - "learning based task offloading for vehicular cloud computing systems"
    - "a survey on mobile edge computing the communication perspective"

Connected through hyperedge HE-5 (induced by source paper "mobile communications computing and caching resources optimization for coded caching with device computing"):
  with members:
    - "bandwidth gain from mobile edge computing and caching in wireless multicast systems"
    - "minimizing latency to support vr social interactions over wireless cellular systems via bandwidth allocation"
    - "optimized base station cache allocation for cloud radio access network with multicast backhaul"
    - "communications caching and computing for mobile virtual reality modeling and tradeoff"
    - "edge computing meets millimeter wave enabled vr paving the way to cutting the cord"
    - "coded caching with distributed storage"
    - "the three primary colors of mobile systems"
    - "optimization and analysis of probabilistic caching in n tier heterogeneous networks"

Connected through hyperedge HE-6 (induced by source paper "communications caching computing tradeoff analysis for bidirectional data computation in mobile edge networks"):
  with members:
    - "bandwidth gain from mobile edge computing and caching in wireless multicast systems"
    - "communications caching and computing for mobile virtual reality modeling and tradeoff"
    - "a survey on mobile edge computing the communication perspective"
    - "mobile edge computing a survey on architecture and computation offloading"
    - "joint pushing and caching for bandwidth utilization maximization in wireless networks"
    - "dynamic computation offloading for mobile edge computing with energy harvesting devices"
    - "the three primary colors of mobile systems"
    - "power delay tradeoff in multi user mobile edge computing systems"

Connected through hyperedge HE-7 (induced by source paper "device vs edge computing for mobile services delay aware decision making to minimize power consumption"):
  with members:
    - "joint task offloading scheduling and transmit power allocation for mobile edge computing systems"
    - "mobile edge computing a survey on architecture and computation offloading"
    - "delay optimal computation task scheduling for mobile edge computing systems"
    - "joint uplink downlink optimization for backhaul limited mobile cloud computing with user scheduling"
    - "decentralized computation offloading game for mobile cloud computing"
    - "cloud offloading for multi radio enabled mobile devices"
    - "efficient multi user computation offloading for mobile edge cloud computing"

Connected through hyperedge HE-8 (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"
    - "joint offloading and computing optimization in wireless powered mobile edge computing systems"
    - "asynchronous methods for deep reinforcement learning"
    - "energy efficient mobile cloud computing powered by wireless energy transfer"
    - "the energy frequency convexity rule modeling and experimental validation on mobile devices"

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)