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: "erasure list exponents for slepian wolf decoding"
  Abstract: We analyze random coding error exponents associated with erasure/list Slepian-Wolf decoding using two different methods and then compare the resulting bounds. The first method follows the well known techniques of Gallager and Forney and the second method is based on a technique of distance enumeration, or more generally, type class enumeration, which is rooted in the statistical mechanics of a disordered system that is related to the random energy model (REM). The second method is guaranteed to yield exponent functions which are at least as tight as those of the first method, and it is demonstrated that for certain combinations of coding rates and thresholds, the bounds of the second method are strictly tighter than those of the first method, by an arbitrarily large factor. In fact, the second method may even yield an infinite exponent at regions where the first method gives finite values. We also discuss the option of variable-rate Slepian-Wolf encoding and demonstrate how it can improve on the resulting exponents.

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"
    - "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"
    - "universal decoding for arbitrary channels relative to a given class of decoding metrics"

Connected through hyperedge HE-2 (induced by source paper "analysis of remaining uncertainties and exponents under various conditional r e nyi entropies"):
  with members:
    - "uniform random number generation from markov chains non asymptotic and asymptotic analyses"
    - "security analysis of varepsilon almost dual universal 2 hash functions smoothing of min entropy versus smoothing of renyi entropy of order 2"
    - "asymptotic estimates in information theory with non vanishing error probabilities"
    - "equivocations exponents and second order coding rates under various r enyi information measures"
    - "asymmetric evaluations of erasure and undetected error probabilities"
    - "list decoding random coding exponents and expurgated exponents"
    - "encoding tasks and renyi entropy"
    - "secure multiterminal source coding with side information at the eavesdropper"

Connected through hyperedge HE-3 (induced by source paper "the generalized stochastic likelihood decoder random coding and expurgated bounds"):
  with members:
    - "the likelihood encoder for lossy compression"
    - "universal decoding for source channel coding with side information"
    - "controllability of networked mimo systems"
    - "list decoding random coding exponents and expurgated exponents"
    - "exact random coding error exponents of optimal bin index decoding"
    - "a technique for deriving one shot achievability results in network information theory"
    - "relations between random coding exponents and the statistical physics of random codes"

Connected through hyperedge HE-4 (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"
    - "universal decoding for arbitrary channels relative to a given class of decoding metrics"
    - "multiuser random coding techniques for mismatched decoding"
    - "communication over individual channels a general framework"
    - "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-5 (induced by source paper "simplified erasure list decoding"):
  with members:
    - "asymmetric evaluations of erasure and undetected error probabilities"
    - "list decoding random coding exponents and expurgated exponents"
    - "expurgated random coding ensembles exponents refinements and connections"
    - "erasure list random coding error exponents are not universally achievable"
    - "a neyman pearson approach to universal erasure and list decoding"

Connected through hyperedge HE-6 (induced by source paper "fixed error asymptotics for erasure and list decoding"):
  with members:
    - "the third order term in the normal approximation for the awgn channel"
    - "list decoding random coding exponents and expurgated exponents"
    - "on the dispersions of three network information theory problems"
    - "a tight upper bound for the third order asymptotics for most discrete memoryless channels"
    - "feedback communication systems with limitations on incremental redundancy"
    - "reliability based error detection for feedback communication with low latency"
    - "the third order term in the normal approximation for singular channels"
    - "the log volume of optimal codes for memoryless channels asymptotically within a few nats"

Connected through hyperedge HE-7 (induced by source paper "asymmetric evaluations of erasure and undetected error probabilities"):
  with members:
    - "asymptotic estimates in information theory with non vanishing error probabilities"
    - "list decoding random coding exponents and expurgated exponents"
    - "unequal message protection asymptotic and non asymptotic tradeoffs"
    - "fixed error asymptotics for erasure and list decoding"
    - "moderate deviations in channel coding"
    - "fixed length lossy compression in the finite blocklength regime"
    - "moderate deviations of lossy source coding for discrete and gaussian sources"
    - "information spectrum approach to second order coding rate in channel coding"

Connected through hyperedge HE-8 (induced by source paper "statistical physics of random binning"):
  with members:
    - "exact random coding error exponents of optimal bin index decoding"
    - "optimum trade offs between the error exponent and the excess rate exponent of variable rate slepian wolf coding"
    - "improved source coding exponents via witsenhausen s rate"
    - "relations between random coding exponents and the statistical physics of random codes"

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)