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 subcarrier and cpu time allocation for mobile edge computing"
  Abstract: In mobile edge computing systems, mobile devices can offload compute-intensive tasks to a nearby cloudlet,so as to save energy and extend battery life. Unlike a fully-fledged cloud, a cloudlet is a small-scale datacenter deployed at a wireless access point, and thus is highly constrained by both radio and compute resources. We show in this paper that separately optimizing the allocation of either compute or radio resource, as most existing works did, is highly suboptimal: the congestion of compute resource leads to the waste of radio resource, and vice versa. To address this problem, we propose a joint scheduling algorithm that allocates both radio and compute resources coordinately. Specifically, we consider a cloudlet in an Orthogonal Frequency-Division Multiplexing Access (OFDMA) system with multiple mobile devices, where we study subcarrier allocation for task offloading and CPU time allocation for task execution in the cloudlet. Simulation results show that the proposed algorithm significantly outperforms per-resource optimization, accommodating more offloading requests while achieving salient energy saving.

Connected through hyperedge HE-1 (induced by source paper "client selection for federated learning with heterogeneous resources in mobile edge"):
  with members:
    - "federated learning with non iid data"
    - "differentially private federated learning a client level perspective"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "millimeter wave vehicular communication to support massive automotive sensing"
    - "communication efficient learning of deep networks from decentralized data"
    - "deep residual learning for image recognition"
    - "joint optimization of radio and computational resources for multicell mobile edge computing"
    - "millimeter wave channel modeling and cellular capacity evaluation"

Connected through hyperedge HE-2 (induced by source paper "dyverse dynamic vertical scaling in multi tenant edge environments"):
  with members:
    - "performance estimation of container based cloud to fog offloading"
    - "feasibility of fog computing"
    - "defog fog computing benchmarks"
    - "realizing edge marketplaces challenges and opportunities"
    - "a survey on mobile edge computing the communication perspective"
    - "enorm a framework for edge node resource management"
    - "dynamic computation offloading for mobile edge computing with energy harvesting devices"
    - "energy efficient resource allocation for mobile edge computation offloading"

Connected through hyperedge HE-3 (induced by source paper "energy efficient joint offloading and wireless resource allocation strategy in multi mec server systems"):
  with members:
    - "a survey on mobile edge computing the communication perspective"
    - "e2m2 energy efficient mobility management in dense small cells with mobile edge computing"
    - "joint offloading and computing optimization in wireless powered mobile edge computing systems"
    - "a fundamental tradeoff between computation and communication in distributed computing"
    - "online learning for offloading and autoscaling in renewable powered mobile edge computing"
    - "computation offloading decisions for reducing completion time"
    - "joint optimization of radio and computational resources for multicell mobile edge computing"
    - "joint optimization of radio resources and code partitioning in mobile cloud computing"

Connected through hyperedge HE-4 (induced by source paper "energy delay cost tradeoff for task offloading in imbalanced edge cloud based computing"):
  with members:
    - "a survey on mobile edge computing the communication perspective"
    - "joint task offloading scheduling and transmit power allocation for mobile edge computing systems"
    - "joint offloading and computing optimization in wireless powered mobile edge computing systems"
    - "joint computation and communication cooperation for mobile edge computing"
    - "joint uplink downlink optimization for backhaul limited mobile cloud computing with user scheduling"
    - "decentralized computation offloading game for mobile cloud computing"
    - "joint optimization of radio and computational resources for multicell mobile edge computing"

Connected through hyperedge HE-5 (induced by source paper "edge computing aware noma for 5g networks"):
  with members:
    - "toward hierarchical mobile edge computing an auction based profit maximization approach"
    - "non orthogonal random access nora for 5g networks"
    - "dynamic user clustering and power allocation for uplink and downlink non orthogonal multiple access noma systems"
    - "uplink non orthogonal multiple access for 5g wireless networks"
    - "joint optimization of radio resources and code partitioning in mobile cloud computing"
    - "a quantitative measure of fairness and discrimination for resource allocation in shared computer systems"

Connected through hyperedge HE-6 (induced by source paper "a generic framework for task offloading in mmwave mec backhaul networks"):
  with members:
    - "a survey on mobile edge computing the communication perspective"
    - "a cooperative scheduling scheme of local cloud and internet cloud for delay aware mobile cloud computing"
    - "efficient multi user computation offloading for mobile edge cloud computing"

Connected through hyperedge HE-7 (induced by source paper "joint offloading and resource allocation in vehicular edge computing and networks"):
  with members:
    - "a survey on mobile edge networks convergence of computing caching and communications"
    - "mobile edge computing a survey on architecture and computation offloading"

Connected through hyperedge HE-8 (induced by source paper "a survey on mobile edge computing the communication perspective"):
  with members:
    - "mobile edge computing fog et al a survey and analysis of security threats and challenges"
    - "joint energy minimization and resource allocation in c ran with mobile cloud"
    - "latency optimization for resource allocation in mobile edge computation offloading"
    - "a survey on mobile edge networks convergence of computing caching and communications"
    - "proactive edge computing in latency constrained fog networks"
    - "computation peer offloading for energy constrained mobile edge computing in small cell networks"
    - "mobile edge computing a survey on architecture and computation offloading"
    - "online placement of multi component applications in edge computing environments"

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)