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: "nonparametric detection of anomalous data streams"
  Abstract: A nonparametric anomalous hypothesis testing problem is investigated, in which there are totally n sequences with s anomalous sequences to be detected. Each typical sequence contains m independent and identically distributed (i.i.d.) samples drawn from a distribution p, whereas each anomalous sequence contains m i.i.d. samples drawn from a distribution q that is distinct from p. The distributions p and q are assumed to be unknown in advance. Distribution-free tests are constructed using maximum mean discrepancy as the metric, which is based on mean embeddings of distributions into a reproducing kernel Hilbert space. The probability of error is bounded as a function of the sample size m, the number s of anomalous sequences and the number n of sequences. It is then shown that with s known, the constructed test is exponentially consistent if m is greater than a constant factor of log n, for any p and q, whereas with s unknown, m should has an order strictly greater than log n. Furthermore, it is shown that no test can be consistent for arbitrary p and q if m is less than a constant factor of log n, thus the order-level optimality of the proposed test is established. Numerical results are provided to demonstrate that our tests outperform (or perform as well as) the tests based on other competitive approaches under various cases.

Connected through hyperedge HE-1 (induced by source paper "asymptotically optimal one and two sample testing with kernels"):
  with members:
    - "robust kullback leibler divergence and universal hypothesis testing for continuous distributions"
    - "mmd gan towards deeper understanding of moment matching network"
    - "m statistic for kernel change point detection"
    - "renyi divergence and kullback leibler divergence"
    - "universal outlier hypothesis testing"
    - "universal and composite hypothesis testing via mismatched divergence"

Connected through hyperedge HE-2 (induced by source paper "k medoids clustering of data sequences with composite distributions"):
  with members:
    - "nonparametric composite hypothesis testing in an asymptotic regime"
    - "universal outlier hypothesis testing"

Connected through hyperedge HE-3 (induced by source paper "linear complexity exponentially consistent tests for universal outlying sequence detection"):
  with members:
    - "universal outlying sequence detection for continuous observations"
    - "universal outlier hypothesis testing"
    - "nonparametric divergence estimation with applications to machine learning on distributions"

Connected through hyperedge HE-4 (induced by source paper "scan b statistic for kernel change point detection"):
  with members:
    - "m statistic for kernel change point detection"
    - "scale up nonlinear component analysis with doubly stochastic gradients"
    - "scalable kernel methods via doubly stochastic gradients"
    - "nonparametric detection of anomalous data via kernel mean embedding"
    - "b tests low variance kernel two sample tests"

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.LG(Machine Learning)