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: "asymptotically optimal matching of multiple sequences to source distributions and training sequences"
  Abstract: Consider a finite set of sources, each producing independent identically distributed observations that follow a unique probability distribution on a finite alphabet. We study the problem of matching a finite set of observed sequences to the set of sources under the constraint that the observed sequences are produced by distinct sources. In general, the number of sequences N may be different from the number of sources M, and only some K <= min{M, N} of the observed sequences may be produced by a source from the set of sources of interest. We consider two versions of the problem-one in which the probability laws of the sources are known and another in which the probability laws of the sources are unspecified but one training sequence from each of the sources is available. We show that both these problems can be solved using a sequence of tests that are allowed to produce no-match decisions. The tests ensure exponential decay of the probabilities of incorrect matching as the sequence lengths increase, and minimize the no-match decisions. Both the tests can be implemented using variants of the minimum weight-matching algorithm applied to a weighted bipartite graph. We also compare the performances obtained using these tests with those obtained using tests that do not consider the constraint that the sequences are produced by distinct sources. For the version of the problem in which the probability laws of the sources are known, we compute the rejection exponents and error exponents of the tests and show that the tests that make use of the constraint have better exponents than the tests that do not make use of this information.

Connected through hyperedge HE-1 (induced by source paper "optimal resolution of change point detection with empirically observed statistics and erasures"):
  with members:
    - "distributed detection with empirically observed statistics"
    - "second order asymptotically optimal statistical classification"
    - "selective review of offline change point detection methods"
    - "time division is optimal for covert communication over some broadcast channels"
    - "fundamental limits of communication with low probability of detection"
    - "covert communication over noisy channels a resolvability perspective"
    - "asymptotic estimates in information theory with non vanishing error probabilities"
    - "moderate deviations in channel coding"

Connected through hyperedge HE-2 (induced by source paper "asymptotic limits of privacy in bayesian time series matching"):
  with members:
    - "asymptotic loss in privacy due to dependency in gaussian traces"
    - "privacy of dependent users against statistical matching"
    - "fundamental limits of invisible flow fingerprinting"
    - "privacy against statistical matching inter user correlation"
    - "towards provably invisible network flow fingerprints"
    - "matching anonymized and obfuscated time series to users profiles"
    - "where you are is who you are user identification by matching statistics"
    - "achieving perfect location privacy in wireless devices using anonymization"

Connected through hyperedge HE-3 (induced by source paper "distributed detection with empirically observed statistics"):
  with members:
    - "second order asymptotically optimal statistical classification"
    - "anonymous heterogeneous distributed detection optimal decision rules error exponents and the price of anonymity"
    - "distributed hypothesis testing with privacy constraints"
    - "hypothesis testing under maximal leakage privacy constraints"
    - "asymmetric evaluations of erasure and undetected error probabilities"
    - "privacy against statistical inference"
    - "data fusion trees for detection does architecture matter"

Connected through hyperedge HE-4 (induced by source paper "evaluation of error probability of classification based on the analysis of the bayes code"):
  with members:
    - "second order asymptotically optimal statistical classification"

Connected through hyperedge HE-5 (induced by source paper "privacy of dependent users against statistical matching"):
  with members:
    - "asymptotic limits of privacy in bayesian time series matching"
    - "a concentration of measure approach to database de anonymization"
    - "single server single message online private information retrieval with side information"
    - "asymptotic loss in privacy due to dependency in gaussian traces"
    - "fundamental limits of database alignment"
    - "community detection with side information exact recovery under the stochastic block model"
    - "matching graphs with community structure a concentration of measure approach"
    - "on the performance of a canonical labeling for matching correlated erdős renyi graphs"

Connected through hyperedge HE-6 (induced by source paper "second order asymptotically optimal statistical classification"):
  with members:
    - "asymptotic estimates in information theory with non vanishing error probabilities"
    - "second order coding rates for channels with state"
    - "optimal algorithms for testing closeness of discrete distributions"
    - "testing closeness of discrete distributions"
    - "information spectrum approach to second order coding rate in channel coding"

Connected through hyperedge HE-7 (induced by source paper "the strongly asynchronous massive access channel"):
  with members:
    - "on the capacity of the slotted strongly asynchronous channel with a bursty user"
    - "capacity of gaussian many access channels"
    - "energy and sampling constrained asynchronous communication"
    - "asynchronous communication capacity bounds and suboptimality of training"
    - "optimal sequential frame synchronization"

Connected through hyperedge HE-8 (induced by source paper "privacy against statistical matching inter user correlation"):
  with members:
    - "a smart home is no castle privacy vulnerabilities of encrypted iot traffic"
    - "matching anonymized and obfuscated time series to users profiles"
    - "where you are is who you are user identification by matching statistics"
    - "achieving perfect location privacy in wireless devices using anonymization"
    - "protecting locations with differential privacy under temporal correlations"

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)