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 "curvature based comparison of two neural networks"):
  Abstract: In this paper we show the similarities and differences of two deep neural networks by comparing the manifolds composed of activation vectors in each fully connected layer of them. The main contribution of this paper includes 1) a new data generating algorithm which is crucial for determining the dimension of manifolds; 2) a systematic strategy to compare manifolds. Especially, we take Riemann curvature and sectional curvature as part of criterion, which can reflect the intrinsic geometric properties of manifolds. Some interesting results and phenomenon are given, which help in specifying the similarities and differences between the features extracted by two networks and demystifying the intrinsic mechanism of deep neural networks.

Includes members:
  Member "efficient processing of deep neural networks a tutorial and survey":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "searching for winograd aware quantized networks")
  Member "the effectiveness of data augmentation in image classification using deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "hyper parameter optimization a review of algorithms and applications")
  Member "curvature aware manifold learning":
    also connected through other hyperedges:
      (none)
  Member "efficient representation of low dimensional manifolds using deep networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "gradient dynamics of shallow univariate relu networks")
  Member "deep nets for local manifold learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "statistical guarantees of generative adversarial networks for distribution estimation")
  Member "convergent learning do different neural networks learn the same representations":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "classifying the classifier dissecting the weight space of 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)