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 "change detection in noisy dynamic networks a spectral embedding approach"):
  Abstract: Change detection in dynamic networks is an important problem in many areas, such as fraud detection, cyber intrusion detection and health care monitoring. It is a challenging problem because it involves a time sequence of graphs, each of which is usually very large and sparse with heterogeneous vertex degrees, resulting in a complex, high dimensional mathematical object. Spectral embedding methods provide an effective way to transform a graph to a lower dimensional latent Euclidean space that preserves the underlying structure of the network. Although change detection methods that use spectral embedding are available, they do not address sparsity and degree heterogeneity that usually occur in noisy real-world graphs and a majority of these methods focus on changes in the behaviour of the overall network. #R##N#In this paper, we adapt previously developed techniques in spectral graph theory and propose a novel concept of applying Procrustes techniques to embedded points for vertices in a graph to detect changes in entity behaviour. Our spectral embedding approach not only addresses sparsity and degree heterogeneity issues, but also obtains an estimate of the appropriate embedding dimension. We call this method CDP (change detection using Procrustes analysis). We demonstrate the performance of CDP through extensive simulation experiments and a real-world application. CDP successfully detects various types of vertex-based changes including (i) changes in vertex degree, (ii) changes in community membership of vertices, and (iii) unusual increase or decrease in edge weight between vertices. The change detection performance of CDP is compared with two other baseline methods that employ alternative spectral embedding approaches. In both cases, CDP generally shows superior performance.

Includes members:
  Member "change detection in dynamic attributed networks":
    also connected through other hyperedges:
      (none)
  Member "a probabilistic framework to node level anomaly detection in communication networks":
    also connected through other hyperedges:
      (none)
  Member "dyngem deep embedding method for dynamic graphs":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "inductive representation learning on temporal graphs")
  Member "link prediction for interdisciplinary collaboration via co authorship network":
    also connected through other hyperedges:
      (none)
  Member "hellrank a hellinger based centrality measure for bipartite social networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "compressive closeness in networks")
  Member "fast change point detection on dynamic social networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "detecting stable communities in link streams at multiple temporal scales")
  Member "detection and localization of change points in temporal networks with the aid of stochastic block models":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "unifying local and global change detection in dynamic networks")
  Member "localization and centrality in networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "factorized graph representations for semi supervised learning from sparse data")

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)