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 log volume of optimal codes for memoryless channels asymptotically within a few nats"
  Abstract: Shannon's analysis of the fundamental capacity limits for memoryless communication channels has been refined over time. In this paper, the maximum volume $M_\avg^*(n,\epsilon)$ of length-$n$ codes subject to an average decoding error probability $\epsilon$ is shown to satisfy the following tight asymptotic lower and upper bounds as $n \to \infty$: \[ \underline{A}_\epsilon + o(1) \le \log M_\avg^*(n,\epsilon) - [nC - \sqrt{nV_\epsilon} \,Q^{-1}(\epsilon) + \frac{1}{2} \log n] \le \overline{A}_\epsilon + o(1) \] where $C$ is the Shannon capacity, $V_\epsilon$ the $\epsilon$-channel dispersion, or second-order coding rate, $Q$ the tail probability of the normal distribution, and the constants $\underline{A}_\epsilon$ and $\overline{A}_\epsilon$ are explicitly identified. This expression holds under mild regularity assumptions on the channel, including nonsingularity. The gap $\overline{A}_\epsilon - \underline{A}_\epsilon$ is one nat for weakly symmetric channels in the Cover-Thomas sense, and typically a few nats for other symmetric channels, for the binary symmetric channel, and for the $Z$ channel. The derivation is based on strong large-deviations analysis and refined central limit asymptotics. A random coding scheme that achieves the lower bound is presented. The codewords are drawn from a capacity-achieving input distribution modified by an $O(1/\sqrt{n})$ correction term.

Connected through hyperedge HE-1 (induced by source paper "finite sample analysis of image registration"):
  with members:
    - "estimators for multivariate information measures in general probability spaces"
    - "end to end unsupervised deformable image registration with a convolutional neural network"
    - "a deep metric for multimodal registration"
    - "fundamental limits in multi image alignment"
    - "demystifying fixed k nearest neighbor information estimators"
    - "asymptotic estimates in information theory with non vanishing error probabilities"
    - "asymptotics and non asymptotics for universal fixed to variable source coding"
    - "channels with cost constraints strong converse and dispersion"

Connected through hyperedge HE-2 (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"
    - "the saddlepoint approximation unified random coding asymptotics for fixed and varying rates"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "second and third order asymptotics of the continuous time poisson channel"):
  with members:
    - "the stochastic calculus approach to multi receiver poisson channels"
    - "fine asymptotics for universal one to one compression of parametric sources"
    - "asymptotic estimates in information theory with non vanishing error probabilities"
    - "asymptotics and non asymptotics for universal fixed to variable source coding"
    - "channels with cost constraints strong converse and dispersion"
    - "a tight upper bound for the third order asymptotics for most discrete memoryless channels"
    - "information spectrum approach to second order coding rate in channel coding"

Connected through hyperedge HE-4 (induced by source paper "channel coding at low capacity"):
  with members:
    - "polar codes with a stepped boundary"
    - "recursive list decoding for reed muller codes"
    - "beta beta bounds finite blocklength analog of the golden formula"
    - "relaxed polar codes"
    - "achieving the uniform rate region of general multiple access channels by polar coding"
    - "coding in the finite blocklength regime bounds based on laplace integrals and their asymptotic approximations"
    - "polar codes for broadcast channels"
    - "asymptotic estimates in information theory with non vanishing error probabilities"

Connected through hyperedge HE-5 (induced by source paper "semi finite length analysis for information theoretic tasks"):
  with members:
    - "physical layer security for rf satellite channels in the finite length regime"
    - "uniform random number generation from markov chains non asymptotic and asymptotic analyses"
    - "strong converse for a degraded wiretap channel via active hypothesis testing"
    - "a tight upper bound for the third order asymptotics for most discrete memoryless channels"
    - "non asymptotic and asymptotic analyses of information processing on markov chains"
    - "exponential decreasing rate of leaked information in universal random privacy amplification"
    - "information spectrum approach to second order coding rate in channel coding"
    - "second order asymptotics in fixed length source coding and intrinsic randomness"

Connected through hyperedge HE-6 (induced by source paper "on the throughput of channels that wear out"):
  with members:
    - "energy harvesting transmitters that heat up throughput maximization under temperature constraints"
    - "subblock constrained codes for real time simultaneous energy and information transfer"
    - "dissipation of information in channels with input constraints"
    - "channels with cost constraints strong converse and dispersion"
    - "a tight upper bound for the third order asymptotics for most discrete memoryless channels"
    - "an information theoretic characterization of channels that die"
    - "channels that heat up"

Connected through hyperedge HE-7 (induced by source paper "the sphere packing bound via augustin s method"):
  with members:
    - "the renyi capacity and center"
    - "the sphere packing bound for memoryless channels"
    - "the sphere packing bound for dspcs with feedback a la augustin"
    - "the augustin capacity and center"
    - "the augustin center and the sphere packing bound for memoryless channels"
    - "strong converse theorems for classes of multimessage multicast networks a renyi divergence approach"
    - "f divergence inequalities"
    - "exact asymptotics for the random coding error probability"

Connected through hyperedge HE-8 (induced by source paper "coding in the finite blocklength regime bounds based on laplace integrals and their asymptotic approximations"):
  with members:
    - "exact asymptotics for the random coding error probability"
    - "on the evaluation of the polyanskiy poor verdu converse bound for finite block length coding in awgn"
    - "the third order term in the normal approximation for the awgn channel"
    - "green communication via type i arq finite block length analysis"
    - "mismatched decoding error exponents second order rates and saddlepoint approximations"
    - "moderate deviations in channel coding"

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)