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 "duality of width and depth of neural networks"):
  Abstract: Here, we report that the depth and the width of a neural network are dual from two perspectives. First, we employ the partially separable representation to determine the width and depth. Second, we use the De Morgan law to guide the conversion between a deep network and a wide network. Furthermore, we suggest the generalized De Morgan law to promote duality to network equivalency.

Includes members:
  Member "effect of depth and width on local minima in deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "large scale representation learning from triplet comparisons")
  Member "linear algebra and duality of neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "generating new pictures in complex datasets with a simple neural network")
  Member "model compression via distillation and quantization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "gan compression efficient architectures for interactive conditional gans")
  Member "universal approximation with quadratic deep networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "no spurious local minima in deep quadratic networks")
  Member "fuzzy logic interpretation of quadratic networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "robust deep neural networks inspired by fuzzy logic")
  Member "the power of deeper networks for expressing natural functions":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "intelligence physics and information the tradeoff between accuracy and simplicity in machine learning")
  Member "toward deeper understanding of neural networks the power of initialization and a dual view on expressivity":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "backward feature correction how deep learning performs deep learning")
  Member "why deep neural networks for function approximation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "neural contextual bandits with ucb based exploration")

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)