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: "smart scheduling and feedback allocation over non stationary wireless channels"
  Abstract: It is well known that opportunistic scheduling algorithms are throughput optimal under dynamic channel and network conditions. However, these algorithms achieve a hypothetical rate region which does not take into account the overhead associated with channel probing and feedback required to obtain the full channel state information at every slot. In this work, we design a joint scheduling and channel probing algorithm by considering the overhead of obtaining the channel state information. We adopt a correlated and non-stationary channel model, which is more realistic than those used in the literature. We use concepts from learning and information theory to accurately track channel variations to minimize the number of channels probed at every slot, while scheduling users to maximize the achievable rate region of the network. More specifically, we employ Gaussian Process Regression that enable us to predict future channel state and also provide a way to measure uncertainty in the learning and prediction. Simulation results show that with the proposed algorithm, the network can carry higher user traffic. The proposed solution can be utilized in more complex and heterogeneous environments such as 5G and beyond networks where the model free approach is needed.

Connected through hyperedge HE-1 (induced by source paper "federated learning under channel uncertainty joint client scheduling and resource allocation"):
  with members:
    - "client selection for federated learning with heterogeneous resources in mobile edge"
    - "wireless network intelligence at the edge"
    - "federated learning for ultra reliable low latency v2v communications"
    - "gaussian processes for nonlinear signal processing an overview of recent advances"

Connected through hyperedge HE-2 (induced by source paper "joint opportunistic scheduling and selective channel feedback"):
  with members:
    - "performance and convergence of multi user online learning"
    - "scheduling with rate adaptation under incomplete knowledge of channel estimator statistics"
    - "exploiting channel memory for joint estimation and scheduling in downlink networks"
    - "rethinking information theory for mobile ad hoc 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.NI(Networking and Internet Architecture)