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 "robustness certificates against adversarial examples for relu networks"):
  Abstract: While neural networks have achieved high performance in different learning tasks, their accuracy drops significantly in the presence of small adversarial perturbations to inputs. Defenses based on regularization and adversarial training are often followed by new attacks to defeat them. In this paper, we propose attack-agnostic robustness certificates for a multi-label classification problem using a deep ReLU network. Although computing the exact distance of a given input sample to the classification decision boundary requires solving a non-convex optimization, we characterize two lower bounds for such distances, namely the simplex certificate and the decision boundary certificate. These robustness certificates leverage the piece-wise linear structure of ReLU networks and use the fact that in a polyhedron around a given sample, the prediction function is linear. In particular, the proposed simplex certificate has a closed-form, is differentiable and is an order of magnitude faster to compute than the existing methods even for deep networks. In addition to theoretical bounds, we provide numerical results for our certificates over MNIST and compare them with some existing upper bounds.

Includes members:
  Member "on the effectiveness of interval bound propagation for training verifiably robust models":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "overfitting in adversarially robust deep learning")
  Member "are adversarial examples inevitable":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "anomalous instance detection in deep learning a survey")
  Member "black box adversarial attacks with limited queries and information":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "universal adversarial attack on attention and the resulting dataset damagenet")
  Member "gradient masking causes clever to overestimate adversarial perturbation size":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "metrics and methods for robustness evaluation of neural networks with generative models")
  Member "efficient neural network robustness certification with general activation functions":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "robust deep reinforcement learning against adversarial perturbations on observations")
  Member "certified defenses against adversarial examples":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "overfitting in adversarially robust deep learning")
  Member "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "adversarial ranking attack and defense")
  Member "evaluating robustness of neural networks with mixed integer programming":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "lossless compression of deep neural networks")

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)