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 "interpretable neuron structuring with graph spectral regularization"):
  Abstract: While neural networks have been used to classify or embed data into lower dimensional spaces, they are often regarded as black boxes with uninterpretable features. Here we propose Graph Spectral Regularization for making hidden layers more interpretable without significantly affecting the performance of their primary task. Taking inspiration from spatial organization and localization of neuron activations in biological networks, we use a graph Laplacian that encourages activations to be smooth either on a predetermined graph or on a feature-space graph learned from the data via co-activations of a hidden layer of the neural network. We show numerous uses for this including cluster indication and visualization in biological and image data sets.

Includes members:
  Member "learning discrete structures for graph neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "an end to end graph convolutional kernel support vector machine")
  Member "a comprehensive survey on graph neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a survey on the expressive power of graph neural networks")
  Member "towards a definition of disentangled representations":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "weakly supervised disentanglement without compromises")
  Member "emergence of invariance and disentanglement in deep representations":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "intelligence physics and information the tradeoff between accuracy and simplicity in machine learning")
  Member "dynamic routing between capsules":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "lightweight residual densely connected convolutional neural network")
  Member "interpretable convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "convolutional occupancy networks")
  Member "right for the right reasons training differentiable models by constraining their explanations":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "counterfactual samples synthesizing for robust visual question answering")
  Member "multiscale hierarchical convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "convolutional neural networks via node varying graph filters")

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)