human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "newma a new method for scalable model free online change point detection"):
  Abstract: We consider the problem of detecting abrupt changes in the distribution of a multi-dimensional time series, with limited computing power and memory. In this paper, we propose a new method for model-free online change-point detection that relies only on fast and light recursive statistics, inspired by the classical Exponential Weighted Moving Average algorithm (EWMA). The proposed idea is to compute two EWMA statistics on the stream of data with different forgetting factors, and to compare them. By doing so, we show that we implicitly compare recent samples with older ones, without the need to explicitly store them. Additionally, we leverage Random Features to efficiently use the Maximum Mean Discrepancy as a distance between distributions. We show that our method is orders of magnitude faster than usual non-parametric methods for a given accuracy.

Includes members:
  Member "random projections through multiple optical scattering approximating kernels at the speed of light":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "optical reservoir computing using multiple light scattering for chaotic systems prediction")
  Member "change point detection methods for body worn video":
    also connected through other hyperedges:
      (none)
  Member "generative moment matching networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "scan b statistic for kernel change point detection":
    also connected through other hyperedges:
      (none)
  Member "detecting change points in the large scale structure of evolving networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "change detection in dynamic attributed networks")
  Member "fastfood approximate kernel expansions in loglinear time":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "approximating spectral clustering via sampling a review")
  Member "a kernel method for the two sample problem":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "do we really need to access the source data source hypothesis transfer for unsupervised domain adaptation")

Question: Please tell me which class the source paper 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)