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: "delay analysis of random scheduling and round robin in small cell networks"
  Abstract: We analyze the delay performance of small cell networks operating under random scheduling (RS) and round robin (RR) protocols. Based on stochastic geometry and queuing theory, we derive accurate and tractable expressions for the distribution of mean delay, which accounts for the impact of random traffic arrivals, queuing interactions, and failed packet retransmissions. Our analysis asserts that RR outperforms RS in terms of mean delay, regardless of traffic statistic. Moreover, the gain from RR is more pronounced in the presence of heavy traffic, which confirms the importance of accounting fairness in the design of scheduling policy. We also find that constrained on the same delay outage probability, RR is able to support more user equipments (UEs) than that of RS, demonstrating it as an appropriate candidate for the traffic scheduling policy of internet-of-things (IoT) network.

Connected through hyperedge HE-1 (induced by source paper "optimizing information freshness in wireless networks a stochastic geometry approach"):
  with members:
    - "timely updates in energy harvesting two hop networks offline and online policies"
    - "age optimal updates of multiple information flows"
    - "optimizing information freshness in wireless networks under general interference constraints"
    - "optimizing age of information in wireless networks with perfect channel state information"
    - "age minimal transmission for energy harvesting sensors with finite batteries online policies"
    - "optimal status update for age of information minimization with an energy harvesting source"
    - "cell edge aware precoding for downlink massive mimo cellular networks"
    - "age optimal information updates in multihop networks"

Connected through hyperedge HE-2 (induced by source paper "scheduling policies for federated learning in wireless networks"):
  with members:
    - "in edge ai intelligentizing mobile edge computing caching and communication by federated learning"
    - "client selection for federated learning with heterogeneous resources in mobile edge"
    - "coded federated computing in wireless networks with straggling devices and imperfect csi"
    - "wireless network intelligence at the edge"
    - "federated learning strategies for improving communication efficiency"
    - "broadband analog aggregation for low latency federated edge learning extended version"
    - "federated learning via over the air computation"
    - "adaptive federated learning in resource constrained edge computing systems"

Connected through hyperedge HE-3 (induced by source paper "locally adaptive scheduling policy for optimizing information freshness in wireless networks"):
  with members:
    - "optimizing information freshness in wireless networks under general interference constraints"
    - "optimizing age of information in wireless networks with perfect channel state information"
    - "cell edge aware precoding for downlink massive mimo cellular networks"
    - "spatiotemporal stochastic modeling of iot enabled cellular networks scalability and stability analysis"
    - "heterogeneous cellular networks with spatio temporal traffic delay analysis and scheduling"
    - "energy efficient design of mimo heterogeneous networks with wireless backhaul"
    - "coverage and rate analysis for millimeter wave cellular networks"
    - "analysis of a proportionally fair and locally adaptive spatial aloha in poisson networks"

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)