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: "on the evaluation of the polyanskiy poor verdu converse bound for finite block length coding in awgn"
  Abstract: A tight converse bound to the channel coding rate in the finite block-length regime and under additive white Gaussian noise conditions was recently proposed by Polyanskiy-Poor–Verdu (PPV). The bound is a generalization of a number of other classical results, and it was also claimed to be equivalent to Shannon’s 1959 cone packing bound. In this paper, we investigate methods for a reliable numerical evaluation of the bound, which is troublesome even for not too large values of the block-length    $n$   , by compactly expressing the Polyanskiy, Poor, and Verdu (PPV) converse bound in terms of non-central chi-squared distributions, and by evaluating those through an integral expression and a corresponding series expansion which exploit a method proposed by Temme. As a result, a robust evaluation method and new insights on the bound’s asymptotics, as well as new approximate expressions, are obtained.

Connected through hyperedge HE-1 (induced by source paper "saddlepoint approximations for short packet wireless communications"):
  with members:
    - "secure short packet communications for mission critical iot applications"
    - "efficient error correcting codes in the short blocklength regime"
    - "short packet downlink transmission with non orthogonal multiple access"
    - "5g wireless network slicing for embb urllc and mmtc a communication theoretic view"
    - "short packets over block memoryless fading channels pilot assisted or noncoherent transmission"
    - "joint sum rate and error probability optimization finite blocklength analysis"
    - "coherent multiple antenna block fading channels at finite blocklength"
    - "on single antenna rayleigh block fading channels at finite blocklength"

Connected through hyperedge HE-2 (induced by source paper "gaussian intersymbol interference channels with mismatch"):
  with members:
    - "on the asymptotic equivalence of circulant and toeplitz matrices"
    - "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"
    - "universal decoding for arbitrary channels relative to a given class of decoding metrics"

Connected through hyperedge HE-3 (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"
    - "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"
    - "the log volume of optimal codes for memoryless channels asymptotically within a few nats"
    - "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)