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: "classification with high dimensional sparse samples"
  Abstract: The task of the binary classification problem is to determine which of two distributions has generated a length-n test sequence. The two distributions are unknown; two training sequences of length TV, one from each distribution, are observed. The distributions share an alphabet of size m, which is significantly larger than n and N. How does Ν,η,ηι affect the probability of classification error? We characterize the achievable error rate in a high-dimensional setting in which Ν,η,ηι all tend to infinity, under the assumption that probability of any symbol is Ο(m−1). The results are: 1) There exists an asymptotically consistent classifier if and only if m = o(min{N2, Nn}). This extends the previous consistency result in [1] to the case N ≠ n. 2) For the sparse sample case where max{n, N} = o(m), finer results are obtained: The best achievable probability of error decays as — log(P e ) = J min {N2, Nn}(l +o(l))/m with J > 0. 3) A weighted coincidence-based classifier has non-zero generalized error exponent J. 4) The l 2 -norm based classifier has J = 0.

Connected through hyperedge HE-1 (induced by source paper "universal outlier hypothesis testing"):
  with members:
    - "nonparametric detection of anomalous data via kernel mean embedding"

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)