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: "using channel output feedback to increase throughput in hybrid arq"
  Abstract: Hybrid automatic repeat request (ARQ) protocols have become common in many packet transmission systems due to their incorporation in various standards. Hybrid-ARQ combines the normal ARQ method with forward error correction (FEC) codes to increase reliability and throughput. In this paper, we look at improving upon this performance using feedback information from the destination, in particular, using a powerful FEC code in conjunction with a proposed linear feedback code for the Rayleigh block fading channels. The new hybrid-ARQ scheme is initially developed for full received packet feedback in a point-to-point link. It is then extended to various multiple-antenna scenarios [e.g., multiple-input single-output (MISO), multiple-input multiple-output (MIMO), etc.] with varying amounts of packet feedback information. Simulations illustrate gains in throughput.

Connected through hyperedge HE-1 (induced by source paper "concatenated coding using linear schemes for gaussian broadcast channels with noisy channel output feedback"):
  with members:
    - "the awgn bc with mac feedback a reduction to noiseless feedback via interaction"
    - "on the noisy feedback capacity of gaussian broadcast channels"
    - "mimo mac bc duality with linear feedback coding schemes"
    - "an achievable rate region for the broadcast channel with feedback"
    - "capacity of all nine models of channel output feedback for the two user interference channel"
    - "lqg control approach to gaussian broadcast channels with feedback"
    - "feedback capacity of the gaussian interference channel to within 2 bits"
    - "on the capacity of the discrete memoryless broadcast channel with feedback"

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)