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 "how convnets model non linear transformations"):
  Abstract: In this paper, we theoretically address three fundamental problems involving deep convolutional networks regarding invariance, depth and hierarchy. We introduce the paradigm of Transformation Networks (TN) which are a direct generalization of Convolutional Networks (ConvNets). Theoretically, we show that TNs (and thereby ConvNets) are can be invariant to non-linear transformations of the input despite pooling over mere local translations. Our analysis provides clear insights into the increase in invariance with depth in these networks. Deeper networks are able to model much richer classes of transformations. We also find that a hierarchical architecture allows the network to generate invariance much more efficiently than a non-hierarchical network. Our results provide useful insight into these three fundamental problems in deep learning using ConvNets.

Includes members:
  Member "global optimality in tensor factorization deep learning and beyond":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a generalization theory of gradient descent for learning over parameterized deep relu networks")
  Member "on the expressive power of deep learning a tensor analysis":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "tensor networks for language modeling")
  Member "on the expressive efficiency of sum product networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a deep conditioning treatment of neural networks")
  Member "why does deep learning work a perspective from group theory":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "short sighted deep learning")
  Member "unsupervised learning of invariant representations in hierarchical architectures":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning non parametric invariances from data with permanent random connectomes")
  Member "learning stable group invariant representations with convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "scattering networks for hybrid representation learning")

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.CV(Computer Vision and Pattern Recognition)