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 "geometry and topology of deep neural networks decision boundaries"):
  Abstract: Geometry and topology of decision regions are closely related with classification performance and robustness against adversarial attacks. In this paper, we use differential geometry and topology to explore theoretically the geometrical and topological properties of decision regions produced by deep neural networks (DNNs). The goals are to obtain some geometrical and topological properties of decision regions for given DNN models, and provide some principled guidances to designing and regularizing DNNs. At first, we give the curvatures of decision boundaries in terms of network weights. Based on the rotation index theorem and Gauss-Bonnet-Chern theorem, we then propose methods to identify the closeness and connectivity of given decision boundaries, and obtain the Euler characteristics of closed ones, all without the need to solve decision boundaries explicitly. Finally, we give necessary conditions on network architectures in order to produce closed decision boundaries, and sufficient conditions on network weights for producing zero curvature (flat or developable) decision boundaries.

Includes members:
  Member "understanding global loss landscape of one hidden layer relu neural networks":
    also connected through other hyperedges:
      (none)
  Member "barcodes as summary of objective function s topology":
    also connected through other hyperedges:
      (none)
  Member "depth creates no more spurious local minima":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "understanding global loss landscape of one hidden layer relu neural networks")
  Member "on connected sublevel sets in deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "understanding global loss landscape of one hidden layer relu neural networks")
  Member "elimination of all bad local minima in deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "understanding global loss landscape of one hidden layer relu neural networks")
  Member "small nonlinearities in activation functions create bad local minima in neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "understanding global loss landscape of one hidden layer relu neural networks")
  Member "on decision regions of narrow deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on the decision boundaries of deep neural networks a tropical geometry perspective")
  Member "learning one hidden layer neural networks under general input distributions":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "understanding global loss landscape of one hidden layer relu neural networks")

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)