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: "a new achievable rate region for the discrete memoryless multiple access channel with noiseless feedback"
  Abstract: A new single-letter achievable rate region is proposed for the two-user discrete memoryless multiple-access channel(MAC) with noiseless feedback. The proposed region includes the Cover-Leung rate region [1], and it is shown that the inclusion is strict. The proof uses a block-Markov superposition strategy based on the observation that the messages of the two users are correlated given the feedback. The rates of transmission are too high for each encoder to decode the other's message directly using the feedback, so they transmit correlated information in the next block to learn the message of one another. They then cooperate in the following block to resolve the residual uncertainty of the decoder. The coding scheme may be viewed as a natural generalization of the Cover-Leung scheme with a delay of one extra block and a pair of additional auxiliary random variables. We compute the proposed rate region for two different MACs and compare the results with other known rate regions for the MAC with feedback. Finally, we show how the coding scheme can be extended to obtain larger rate regions with more auxiliary random variables.

Connected through hyperedge HE-1 (induced by source paper "an achievable rate region for the broadcast channel with feedback"):
  with members:
    - "feedback can double the prelog of some memoryless gaussian networks"
    - "on the capacity of the discrete memoryless broadcast channel with feedback"
    - "correlated sources over broadcast channels"
    - "comments on broadcast channels with arbitrarily correlated sources"
    - "approximate characterizations for the gaussian source broadcast distortion region"
    - "broadcasting correlated gaussians"
    - "a graph based framework for transmission of correlated sources over broadcast channels"

Connected through hyperedge HE-2 (induced by source paper "on optimal causal coding of partially observed markov sources in single and multi terminal settings"):
  with members:
    - "optimal decentralized control of coupled subsystems with control sharing"
    - "optimal control strategies in delayed sharing information structures"
    - "on the capacity of memoryless finite state multiple access channels with asymmetric state information at the encoders"

Connected through hyperedge HE-3 (induced by source paper "typicality graphs large deviation analysis"):
  with members:
    - "a graph based framework for transmission of correlated sources over broadcast channels"

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)