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 "electron proton dynamics in deep learning"):
  Abstract: We study the efficacy of learning neural networks with neural networks by the (stochastic) gradient descent method. While gradient descent enjoys empirical success in a variety of applications, there is a lack of theoretical guarantees that explains the practical utility of deep learning. We focus on two-layer neural networks with a linear activation on the output node. We show that under some mild assumptions and certain classes of activation functions, gradient descent does learn the parameters of the neural network and converges to the global minima. Using a node-wise gradient descent algorithm, we show that learning can be done in finite, sometimes $poly(d,1/\epsilon)$, time and sample complexity.

Includes members:
  Member "learning one hidden layer neural networks with landscape design":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "backward feature correction how deep learning performs deep learning")
  Member "beating the perils of non convexity guaranteed training of neural networks using tensor methods":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "no spurious local minima in deep quadratic networks")
  Member "globally optimal gradient descent for a convnet with gaussian inputs":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "backward feature correction how deep learning performs deep learning")
  Member "sgd learns the conjugate kernel class of the network":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "backward feature correction how deep learning performs deep learning")
  Member "an analytical formula of population gradient for two layered relu network and its applications in convergence and critical point analysis":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "backward feature correction how deep learning performs deep learning")
  Member "how to escape saddle points efficiently":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "understanding global loss landscape of one hidden layer relu neural networks")
  Member "convergence analysis of two layer neural networks with relu activation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "backward feature correction how deep learning performs deep learning")
  Member "deep residual networks with exponential linear unit":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "crl class representative learning for image classification")

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.DS(Data Structures and Algorithms)