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: "m statistic for kernel change point detection"
  Abstract: Detecting the emergence of an abrupt change-point is a classic problem in statistics and machine learning. Kernel-based nonparametric statistics have been proposed for this task which make fewer assumptions on the distributions than traditional parametric approach. However, none of the existing kernel statistics has provided a computationally efficient way to characterize the extremal behavior of the statistic. Such characterization is crucial for setting the detection threshold, to control the significance level in the offline case as well as the false alarm rate (captured by the average run length) in the online case. In this paper we focus on the scenario when the amount of background data is large, and propose two related computationally efficient kernel-based statistics for change-point detection, which we call "$M$-statistics". A novel theoretical result of the paper is the characterization of the tail probability of these statistics using a new technique based on change-of-measure. Such characterization provides us accurate detection thresholds for both offline and online cases in computationally efficient manner, without the need to resort to the more expensive simulations such as bootstrapping. Moreover, our $M$-statistic can be applied to high-dimensional data by choosing a proper kernel. We show that our methods perform well in both synthetic and real world data.

Connected through hyperedge HE-1 (induced by source paper "deep learning for multi scale changepoint detection in multivariate time series"):
  with members:
    - "wavelet convolutional neural networks"
    - "skip rnn learning to skip state updates in recurrent neural networks"
    - "feature pyramid networks for object detection"
    - "deep convolutional and recurrent models for human activity recognition using wearables"
    - "hierarchical multiscale recurrent neural networks"
    - "end to end memory networks"
    - "adam a method for stochastic optimization"
    - "going deeper with convolutions"

Connected through hyperedge HE-2 (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"
    - "nonparametric detection of anomalous data streams"
    - "renyi divergence and kullback leibler divergence"
    - "universal outlier hypothesis testing"
    - "universal and composite hypothesis testing via mismatched divergence"

Connected through hyperedge HE-3 (induced by source paper "scan b statistic for kernel change point detection"):
  with members:
    - "scale up nonlinear component analysis with doubly stochastic gradients"
    - "nonparametric detection of anomalous data streams"
    - "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)