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 "tensor entropy for uniform hypergraphs"):
  Abstract: In this paper, we develop the notion of entropy for uniform hypergraphs via tensor theory. We employ the probability distribution of the generalized singular values, calculated from the higher-order singular value decomposition of the Laplacian tensors, to fit into the Shannon entropy formula. We show that this tensor entropy is an extension of von Neumann entropy for graphs. In addition, we establish results on the lower and upper bounds of the entropy and demonstrate that it is a measure of regularity for uniform hypergraphs in simulated and experimental data. We exploit the tensor train decomposition in computing the proposed tensor entropy efficiently. Finally, we introduce the notion of robustness for uniform hypergraphs.

Includes members:
  Member "searching for representative modes on hypergraphs for robust geometric model fitting":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "progressive x efficient anytime multi model fitting algorithm")
  Member "principal component analysis with tensor train subspace":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "tensor train parameterization for ultra dimensionality reduction")
  Member "tensor networks for dimensionality reduction and large scale optimization part 2 applications and future perspectives":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "tensor decompositions in deep learning")
  Member "the resistance perturbation distance a metric for the analysis of dynamic networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "detecting topological changes in dynamic community networks")
  Member "graph based anomaly detection and description a survey":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "transferring robustness for graph neural network against poisoning attacks")
  Member "most tensor problems are np hard":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "provable tensor ring completion")

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)