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 "adaptive versus standard descent methods and robustness against adversarial examples"):
  Abstract: Adversarial examples are a pervasive phenomenon of machine learning models where seemingly imperceptible perturbations to the input lead to misclassifications for otherwise statistically accurate models. In this paper we study how the choice of optimization algorithm influences the robustness of the resulting classifier to adversarial examples. Specifically we show an example of a learning problem for which the solution found by adaptive optimization algorithms exhibits qualitatively worse robustness properties against both $L_{2}$- and $L_{\infty}$-adversaries than the solution found by non-adaptive algorithms. Then we fully characterize the geometry of the loss landscape of $L_{2}$-adversarial training in least-squares linear regression. The geometry of the loss landscape is subtle and has important consequences for optimization algorithms. Finally we provide experimental evidence which suggests that non-adaptive methods consistently produce more robust models than adaptive methods.

Includes members:
  Member "sgd converges to global minimum in deep learning via star convex path":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "training neural networks for and by interpolation")
  Member "cross entropy loss and low rank features have responsibility for adversarial examples":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "understanding the decision boundary of deep neural networks an empirical study")
  Member "theoretically principled trade off between robustness and accuracy":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "overfitting in adversarially robust deep learning")
  Member "adversarial training with voronoi constraints":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "robustness for non parametric classification a generic attack and defense")
  Member "on the geometry of adversarial examples":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "vulnerabilities of connectionist ai applications evaluation and defence")
  Member "gradient descent finds global minima of deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "backward feature correction how deep learning performs deep learning")
  Member "implicit bias of gradient descent on linear convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "disentangling adaptive gradient methods from learning rates")
  Member "poincar e glove hyperbolic word embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "binarized pmi matrix bridging word embeddings and hyperbolic spaces")

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)