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: "second order asymptotically optimal statistical classification"
  Abstract: Motivated by real-world machine learning applications, we analyze approximations to the non-asymptotic fundamental limits of statistical classification. In the binary version of this problem, given two training sequences generated according to two {\em unknown} distributions $P_1$ and $P_2$, one is tasked to classify a test sequence which is known to be generated according to either $P_1$ or $P_2$. This problem can be thought of as an analogue of the binary hypothesis testing problem but in the present setting, the generating distributions are unknown. Due to finite sample considerations, we consider the second-order asymptotics (or dispersion-type) tradeoff between type-I and type-II error probabilities for tests which ensure that (i) the type-I error probability for {\em all} pairs of distributions decays exponentially fast and (ii) the type-II error probability for a {\em particular} pair of distributions is non-vanishing. We generalize our results to classification of multiple hypotheses with the rejection option.

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"
    - "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"
    - "asymptotically optimal matching of multiple sequences to source distributions and training sequences"
    - "moderate deviations in channel coding"

Connected through hyperedge HE-2 (induced by source paper "distributed detection with empirically observed statistics"):
  with members:
    - "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"
    - "asymptotically optimal matching of multiple sequences to source distributions and training sequences"
    - "asymmetric evaluations of erasure and undetected error probabilities"
    - "privacy against statistical inference"
    - "data fusion trees for detection does architecture matter"

Connected through hyperedge HE-3 (induced by source paper "evaluation of error probability of classification based on the analysis of the bayes code"):
  with members:
    - "asymptotically optimal matching of multiple sequences to source distributions and training sequences"

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)