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 "deep network classification by scattering and homotopy dictionary learning"):
  Abstract: We introduce a sparse scattering deep convolutional neural network, which provides a simple model to analyze properties of deep representation learning for classification. Learning a single dictionary matrix with a classifier yields a higher classification accuracy than AlexNet over the ImageNet ILSVRC2012 dataset. The network first applies a scattering transform which linearizes variabilities due to geometric transformations such as translations and small deformations. A sparse l1 dictionary coding reduces intra-class variability while preserving class separation through projections over unions of linear spaces. It is implemented in a deep convolutional network with a homotopy algorithm having an exponential convergence. A convergence proof is given in a general framework including ALISTA. Classification results are analyzed over ImageNet.

Includes members:
  Member "deep dictionary learning a parametric network approach":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deep transform and metric learning network wedding deep dictionary learning and neural networks")
  Member "scattering networks for hybrid representation learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "harmonic convolutional networks based on discrete cosine transform")
  Member "multilayer convolutional sparse modeling pursuit and dictionary learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "algorithmic guarantees for inverse imaging with untrained network priors")
  Member "kymatio scattering transforms in python":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "biologically inspired architectures for sample efficient deep reinforcement learning")
  Member "scaling the scattering transform deep hybrid networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "analyzing the dependency of convnets on spatial information")
  Member "supervised deep sparse coding networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "convolutional dictionary pair learning network for image representation learning")
  Member "deep residual learning for image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "imagenet large scale visual recognition challenge":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")

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)