human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "gaussian intersymbol interference channels with mismatch"):
  Abstract: This paper considers the problem of channel coding over Gaussian intersymbol interference (ISI) channels with a given metric decoding rule. Specifically, it is assumed that the mismatched decoder has an incorrect assumption on the impulse response function. The mismatch capacity is the highest achievable rate for a given decoding rule. Existing lower bounds to the mismatch capacity for channels and decoding metrics with memory (as in our model) are presented only in the form of multi-letter expressions that have not been calculated in practice. Consequently, they provide little insight on the mismatch problem. In this paper, we derive computable single-letter lower bounds to the mismatch capacity, and discuss some implications of our results. Our achievable rates are based on two ensembles, the ensemble of codewords generated by an autoregressive process, and the ensemble of codewords drawn uniformly over a "type class" of real-valued sequences. Computation of our achievable rates demonstrates non-trivial behavior of the achievable rates as a function of the mismatched parameters. As a simple application of our technique, we derive also the random coding exponent associated with a mismatched decoder which assumes that there is no ISI at all. Finally, we compare our results with universal decoders which are designed outside the true class of channels that we consider in this paper.

Includes members:
  Member "on the asymptotic equivalence of circulant and toeplitz matrices":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "uplink downlink channel covariance transformations and precoding design for fdd massive mimo")
  Member "on the evaluation of the polyanskiy poor verdu converse bound for finite block length coding in awgn":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "saddlepoint approximations for short packet wireless communications")
  Member "random coding error exponents for the two user interference channel":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "exact error and erasure exponents for the asymmetric broadcast channel")
  Member "universal decoding for gaussian intersymbol interference channels":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a universal decoder relative to a given family of metrics")
  Member "erasure list exponents for slepian wolf decoding":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "analysis of remaining uncertainties and exponents under various conditional r e nyi entropies")
  Member "mismatched decoding error exponents second order rates and saddlepoint approximations":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "saddlepoint approximations for short packet wireless communications")
  Member "a general formula for the mismatch capacity":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "strategic communication with side information at the decoder")
  Member "analysis of mismatched estimation errors using gradients of partition functions":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "on communication through a gaussian channel with an mmse disturbance constraint")

Question: Please tell me which class the source paper 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)