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: "communication over individual channels a general framework"
  Abstract: We consider the problem of communicating over a channel for which no mathematical model is specified, and the achievable rates are determined as a function of the channel input and output sequences known a-posteriori, without assuming any a-priori relation between them. In a previous paper we have shown that the empirical mutual information between the input and output sequences is achievable without specifying the channel model, by using feedback and common randomness, and a similar result for real-valued input and output alphabets. In this paper, we present a unifying framework which includes the two previous results as particular cases. We characterize the region of rate functions which are achievable, and show that asymptotically the rate function is equivalent to a conditional distribution of the channel input given the output. We present a scheme that achieves these rates with asymptotically vanishing overheads.

Connected through hyperedge HE-1 (induced by source paper "gaussian intersymbol interference channels with mismatch"):
  with members:
    - "on the asymptotic equivalence of circulant and toeplitz matrices"
    - "on the evaluation of the polyanskiy poor verdu converse bound for finite block length coding in awgn"
    - "random coding error exponents for the two user interference channel"
    - "universal decoding for gaussian intersymbol interference channels"
    - "erasure list exponents for slepian wolf decoding"
    - "mismatched decoding error exponents second order rates and saddlepoint approximations"
    - "a general formula for the mismatch capacity"
    - "analysis of mismatched estimation errors using gradients of partition functions"

Connected through hyperedge HE-2 (induced by source paper "universal decoding using a noisy codebook"):
  with members:
    - "reliability of universal decoding based on vector quantized codewords"
    - "universal decoding for arbitrary channels relative to a given class of decoding metrics"
    - "communication over individual channels"

Connected through hyperedge HE-3 (induced by source paper "universal decoding for source channel coding with side information"):
  with members:
    - "exact random coding error exponents of optimal bin index decoding"
    - "erasure list exponents for slepian wolf decoding"
    - "universal decoding for arbitrary channels relative to a given class of decoding metrics"
    - "multiuser random coding techniques for mismatched decoding"
    - "the porosity of additive noise sequences"
    - "communication over individual channels"
    - "universal communication part i modulo additive channels"
    - "relations between random coding exponents and the statistical physics of random codes"

Connected through hyperedge HE-4 (induced by source paper "a universal decoder relative to a given family of metrics"):
  with members:
    - "universal decoding for gaussian intersymbol interference channels"
    - "universal decoding for arbitrary channels relative to a given class of decoding metrics"
    - "universal communication over arbitrarily varying channels"
    - "universal communication part i modulo additive channels"
    - "blind decoding of linear gaussian channels with isi capacity error exponent universality"

Connected through hyperedge HE-5 (induced by source paper "universal decoding for gaussian intersymbol interference channels"):
  with members:
    - "asymptotic mmse analysis under sparse representation modeling"
    - "analysis of mismatched estimation errors using gradients of partition functions"
    - "universal decoding for arbitrary channels relative to a given class of decoding metrics"
    - "the porosity of additive noise sequences"
    - "universal communication part i modulo additive channels"

Connected through hyperedge HE-6 (induced by source paper "universal decoding for arbitrary channels relative to a given class of decoding metrics"):
  with members:
    - "the porosity of additive noise sequences"
    - "universal communication part i modulo additive channels"

Connected through hyperedge HE-7 (induced by source paper "universal prior prediction for communication"):
  with members:
    - "communication over individual channels"
    - "zero rate feedback can achieve the empirical capacity"

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)