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 generation of unrestricted adversarial inputs"):
  Abstract: Neural networks are vulnerable to adversarially-constructed perturbations of their inputs. Most research so far has considered perturbations of a fixed magnitude under some $l_p$ norm. Although studying these attacks is valuable, there has been increasing interest in the construction of (and robustness to) unrestricted attacks, which are not constrained to a small and rather artificial subset of all possible adversarial inputs. We introduce a novel algorithm for generating such unrestricted adversarial inputs which, unlike prior work, is adaptive: it is able to tune its attacks to the classifier being targeted. It also offers a 400-2,000x speedup over the existing state of the art. We demonstrate our approach by generating unrestricted adversarial inputs that fool classifiers robust to perturbation-based attacks. We also show that, by virtue of being adaptive and unrestricted, our attack is able to defeat adversarial training against it.

Includes members:
  Member "algorithms for verifying deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "parot a practical framework for robust deep neural network training")
  Member "at gan a generative attack model for adversarial transferring on generative adversarial nets":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "nesterov accelerated gradient and scale invariance for adversarial attacks")
  Member "a general framework for adversarial examples with objectives":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "advjnd generating adversarial examples with just noticeable difference")
  Member "adversarial attacks and defenses in images graphs and text a review":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "anomalous instance detection in deep learning a survey")
  Member "global adversarial attacks for assessing deep learning robustness":
    also connected through other hyperedges:
      (none)
  Member "how good is my gan":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "adaptive density estimation for generative models")
  Member "rmdl random multimodel deep learning for classification":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a branching and merging convolutional network with homogeneous filter capsules")
  Member "towards the first adversarially robust neural network model on mnist":
    also connected through other hyperedges:
      - Hyperedge HE-7 (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)