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: "the saddlepoint approximation unified random coding asymptotics for fixed and varying rates"
  Abstract: This paper presents a saddlepoint approximation of the random-coding union bound of Polyanskiy et al. for i.i.d. random coding over discrete memoryless channels. The approximation is single-letter, and can thus be computed efficiently. Moreover, it is shown to be asymptotically tight for both fixed and varying rates, unifying existing achievability results in the regimes of error exponents, second-order coding rates, and moderate deviations. For fixed rates, novel exact-asymptotics expressions are specified to within a multiplicative 1+o(1) term. A numerical example is provided for which the approximation is remarkably accurate even at short block lengths.

Connected through hyperedge HE-1 (induced by source paper "on exact asymptotics of the error probability in channel coding symmetric channels"):
  with members:
    - "a new method for employing feedback to improve coding performance"
    - "the sphere packing bound for dspcs with feedback a la augustin"
    - "exact asymptotics for the random coding error probability"
    - "a tight upper bound for the third order asymptotics for most discrete memoryless channels"
    - "mismatched decoding error exponents second order rates and saddlepoint approximations"
    - "refinement of the random coding bound"
    - "a derivation of the asymptotic random coding prefactor"
    - "the log volume of optimal codes for memoryless channels asymptotically within a few nats"

Connected through hyperedge HE-2 (induced by source paper "asymptotic estimates in information theory with non vanishing error probabilities"):
  with members:
    - "second order asymptotics for the gaussian mac with degraded message sets"
    - "the third order term in the normal approximation for the awgn channel"
    - "strong converse and second order asymptotics of channel resolvability"
    - "channels with cost constraints strong converse and dispersion"
    - "secret key agreement general capacity and second order asymptotics"
    - "second order coding rates for channels with state"
    - "unequal message protection asymptotic and non asymptotic tradeoffs"
    - "information spectrum approach to strong converse theorems for degraded wiretap channels"

Connected through hyperedge HE-3 (induced by source paper "exact asymptotics for the random coding error probability"):
  with members:
    - "refinement of the random coding bound"
    - "information spectrum approach to second order coding rate in channel coding"

Connected through hyperedge HE-4 (induced by source paper "non asymptotic and asymptotic analyses of information processing on markov chains"):
  with members:
    - "security analysis of varepsilon almost dual universal 2 hash functions smoothing of min entropy versus smoothing of renyi entropy of order 2"
    - "second order coding rates for channels with state"
    - "mismatched decoding error exponents second order rates and saddlepoint approximations"
    - "non asymptotic analysis of privacy amplification via renyi entropy and inf spectral entropy"
    - "exponential decreasing rate of leaked information in universal random privacy amplification"
    - "moderate deviations of lossy source coding for discrete and gaussian sources"
    - "information spectrum approach to second order coding rate in channel coding"
    - "second order asymptotics in fixed length source coding and intrinsic randomness"

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)