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 robustness to adversarial examples and polynomial optimization"):
  Abstract: We study the design of computationally efficient algorithms with provable guarantees, that are robust to adversarial (test time) perturbations. While there has been an proliferation of recent work on this topic due to its connections to test time robustness of deep networks, there is limited theoretical understanding of several basic questions like (i) when and how can one design provably robust learning algorithms? (ii) what is the price of achieving robustness to adversarial examples in a computationally efficient manner? #R##N#The main contribution of this work is to exhibit a strong connection between achieving robustness to adversarial examples, and a rich class of polynomial optimization problems, thereby making progress on the above questions. In particular, we leverage this connection to (a) design computationally efficient robust algorithms with provable guarantees for a large class of hypothesis, namely linear classifiers and degree-2 polynomial threshold functions (PTFs), (b) give a precise characterization of the price of achieving robustness in a computationally efficient manner for these classes, (c) design efficient algorithms to certify robustness and generate adversarial attacks in a principled manner for 2-layer neural networks. We empirically demonstrate the effectiveness of these attacks on real data.

Includes members:
  Member "on the hardness of robust classification":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "randomization matters how to defend against strong adversarial attacks")
  Member "the curse of concentration in robust learning evasion and poisoning attacks from concentration of measure":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "utilizing network properties to detect erroneous inputs")
  Member "adversarial examples from cryptographic pseudo random generators":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "adversarially robust low dimensional representations")
  Member "adversarial risk and robustness general definitions and implications for the uniform distribution":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "more data can expand the generalization gap between adversarially robust and standard models")
  Member "improved generalization bounds for robust learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "adversarially robust low dimensional representations")
  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 "motivating the rules of the game for adversarial example research":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "vulnerabilities of connectionist ai applications evaluation and defence")
  Member "robustness of classifiers from adversarial to random noise":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "increasing the robustness of dnns against image corruptions by playing the game of noise")

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)