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: "selective review of offline change point detection methods"
  Abstract: This article presents a selective survey of algorithms for the offline detection of multiple change points in multivariate time series. A general yet structuring methodological strategy is adopted to organize this vast body of work. More precisely, detection algorithms considered in this review are characterized by three elements: a cost function, a search method and a constraint on the number of changes. Each of those elements is described, reviewed and discussed separately. Implementations of the main algorithms described in this article are provided within a Python package called ruptures.

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"
    - "second order asymptotically optimal statistical classification"
    - "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 "unsupervised non parametric change point detection in quasi periodic signals"):
  with members:
    - "an unsupervised feature learning approach to reduce false alarm rate in icus"

Connected through hyperedge HE-3 (induced by source paper "bayesian online detection and prediction of change points"):
  with members:
    - "segmental recurrent neural networks"

Connected through hyperedge HE-4 (induced by source paper "differentiable algorithm for marginalising changepoints"):
  with members:
    - "design and implementation of probabilistic programming language anglican"

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.CE(Computational Engineering, Finance, and Science)