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 "on the quality of the initial basin in overspecified neural networks"):
  Abstract: Deep learning, in the form of artificial neural networks, has achieved remarkable practical success in recent years, for a variety of difficult machine learning applications. However, a theoretical explanation for this remains a major open problem, since training neural networks involves optimizing a highly non-convex objective function, and is known to be computationally hard in the worst case. In this work, we study the \emph{geometric} structure of the associated non-convex objective function, in the context of ReLU networks and starting from a random initialization of the network parameters. We identify some conditions under which it becomes more favorable to optimization, in the sense of (i) High probability of initializing at a point from which there is a monotonically decreasing path to a global minimum; and (ii) High probability of initializing at a basin (suitably defined) with a small minimal objective value. A common theme in our results is that such properties are more likely to hold for larger ("overspecified") networks, which accords with some recent empirical and theoretical observations.

Includes members:
  Member "beating the perils of non convexity guaranteed training of neural networks using tensor methods":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "no spurious local minima in deep quadratic networks")
  Member "learning halfspaces and neural networks with random initialization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning two layer networks with multinomial activation and high thresholds")
  Member "global optimality in tensor factorization deep learning and beyond":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a generalization theory of gradient descent for learning over parameterized deep relu networks")
  Member "qualitatively characterizing neural network optimization problems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "gradient surgery for multi task learning")
  Member "on the computational efficiency of training neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "mse optimal neural network initialization via layer fusion")
  Member "the loss surfaces of multilayer networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "sideways depth parallel training of video models")
  Member "identifying and attacking the saddle point problem in high dimensional non convex optimization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "understanding global loss landscape of one hidden layer relu neural networks")
  Member "breaking the curse of dimensionality with convex neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "good subnetworks provably exist pruning via greedy forward selection")

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)