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: "balancing forward and feedback error correction for erasure channels with unreliable feedback"
  Abstract: The traditional information theoretic approach to studyin g feedback is to consider ideal instantaneous high-rate feedback of the channel outputs to the encoder. This was acceptable in classical work because the results were negative: Shannon pointed out that even perfect feedback often does not improve capacity and in the context of symmetric DMCs, Dobrushin showed that it does not improve the fixed block-coding error exponents in the interesting high rate regime. However, it has recently been shown that perfect feedback does allow great improvements in the asymptotic tradeoff between end-to-end delay and probability of error, even for symmetric channels at high rate. Since gains are claimed with ideal instantaneous feed back, it is natural to wonder whether these improvements remain if the feedback is unreliable or otherwise limited. Here, packet-erasure channels are considered on both the forward and feedback links. First, the feedback channel is considered as a given and a strategy is given to balance forward and feedback error correction in the suitable information-theoretic limit of long end-to-end d elays. At high enough rates, perfect-feedback performance is asymptotically attainable despite having only unreliab le feedback! Second, the results are interpreted in the zero sum case of “half-duplex” nodes where the allocation of bandwidth or time to the feedback channel comes at the direct expense of the forward channel. It turns out that even here, feedback is worthwhile since dramatically lower asymptotic delays are possible by appropriately balancing forward and feedback error correction. The results easily generalize to channels with strictly pos itive zero-undeclared-error capacities.

Connected through hyperedge HE-1 (induced by source paper "zero rate feedback can achieve the empirical capacity"):
  with members:
    - "on the gaussian mac with imperfect feedback"
    - "feedback communication over individual channels"
    - "rateless codes for avc models"
    - "feedback capacity of stationary gaussian channels"
    - "a counterexample to cover s 2p conjecture on gaussian feedback capacity"

Connected through hyperedge HE-2 (induced by source paper "rateless codes for avc models"):
  with members:
    - "zero rate feedback can achieve the empirical capacity"
    - "rateless coding for gaussian channels"
    - "coding into a source a direct inverse rate distortion theorem"

Connected through hyperedge HE-3 (induced by source paper "why block length and delay behave differently if feedback is present"):
  with members:
    - "the price of ignorance the impact of side information on delay for lossless source coding"
    - "channel combining and splitting for cutoff rate improvement"

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)