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 "learning halfspaces and neural networks with random initialization"):
  Abstract: We study non-convex empirical risk minimization for learning halfspaces and neural networks. For loss functions that are $L$-Lipschitz continuous, we present algorithms to learn halfspaces and multi-layer neural networks that achieve arbitrarily small excess risk $\epsilon>0$. The time complexity is polynomial in the input dimension $d$ and the sample size $n$, but exponential in the quantity $(L/\epsilon^2)\log(L/\epsilon)$. These algorithms run multiple rounds of random initialization followed by arbitrary optimization steps. We further show that if the data is separable by some neural network with constant margin $\gamma>0$, then there is a polynomial-time algorithm for learning a neural network that separates the training data with margin $\Omega(\gamma)$. As a consequence, the algorithm achieves arbitrary generalization error $\epsilon>0$ with ${\rm poly}(d,1/\epsilon)$ sample and time complexity. We establish the same learnability result when the labels are randomly flipped with probability $\eta<1/2$.

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 "sparse activity and sparse connectivity in supervised learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "shakeout a new approach to regularized deep neural network training")
  Member "efficient learning of linear separators under bounded noise":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "efficient active learning of sparse halfspaces with arbitrary bounded noise")
  Member "ell_1 regularized neural networks are improperly learnable in polynomial time":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "generalised lipschitz regularisation equals distributional robustness")
  Member "norm based capacity control in neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a deep conditioning treatment of neural networks")
  Member "provable methods for training neural networks with sparse connectivity":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on connected sublevel sets in deep learning")
  Member "provable bounds for learning some deep representations":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "span recovery for deep neural networks with applications to input obfuscation")
  Member "from average case complexity to improper learning complexity":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "time space tradeoffs for distinguishing distributions and applications to security of goldreich s prg")

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)