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: "asymptotically optimal resource block allocation with limited feedback"
  Abstract: Consider a channel allocation problem over a frequency-selective channel. There are    $K$    channels (frequency bands) and    $N$    users such that    $K=bN$    for some positive integer    $b$   . We want to allocate    $b$    channels (or resource blocks) to each user. Due to the nature of the frequency-selective channel, each user considers some channels to be better than others. The optimal solution to this resource allocation problem can be computed using the Hungarian algorithm. However, this requires knowledge of the numerical value of all the channel gains, which makes this approach impractical for large networks. We suggest a suboptimal approach that only requires knowing what the    $M$   -best channels of each user are. We find the minimal value of    $M$    such that there exists an allocation where all the    $b$    channels each user gets are among his    $M$   -best. This leads to the feedback of significantly less than one bit per user per channel. For a large class of fading distributions, including Rayleigh, Rician, m-Nakagami, and others, this suboptimal approach leads to both an asymptotically (in    $K$   ) optimal sum rate and an asymptotically optimal minimal rate. Our non-opportunistic approach achieves (asymptotically) full multiuser diversity as well as optimal fairness in contrast to all other limited feedback algorithms.

Connected through hyperedge HE-1 (induced by source paper "distributed energy efficient channel allocation"):
  with members:
    - "distributed learning for channel allocation over a shared spectrum"
    - "game theoretic dynamic channel allocation for frequency selective interference channels"
    - "a survey of energy efficient techniques for 5g networks and challenges ahead"
    - "distributed channel assignment in cognitive radio networks stable matching and walrasian equilibrium"
    - "matching theory for future wireless networks fundamentals and applications"
    - "fully distributed optimal channel assignment for open spectrum access"
    - "on optimizing energy efficiency in multi radio multi channel wireless networks"
    - "framework for link level energy efficiency optimization with informed transmitter"

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)