human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "enhancing gradient based attacks with symbolic intervals"
  Abstract: Recent breakthroughs in defenses against adversarial examples, like adversarial training, make the neural networks robust against various classes of attackers (e.g., first-order gradient-based attacks). However, it is an open question whether the adversarially trained networks are truly robust under unknown attacks. In this paper, we present interval attacks, a new technique to find adversarial examples to evaluate the robustness of neural networks. Interval attacks leverage symbolic interval propagation, a bound propagation technique that can exploit a broader view around the current input to locate promising areas containing adversarial instances, which in turn can be searched with existing gradient-guided attacks. We can obtain such a broader view using sound bound propagation methods to track and over-approximate the errors of the network within given input ranges. Our results show that, on state-of-the-art adversarially trained networks, interval attack can find on average 47% relatively more violations than the state-of-the-art gradient-guided PGD attack.

Connected through hyperedge HE-1 (induced by source paper "certified defenses for adversarial patches"):
  with members:
    - "adversarial training and robustness for multiple perturbations"
    - "certified adversarial robustness via randomized smoothing"
    - "wasserstein adversarial examples via projected sinkhorn iterations"
    - "adversarial training for free"
    - "towards stable and efficient training of verifiably robust neural networks"
    - "on the effectiveness of interval bound propagation for training verifiably robust models"
    - "local gradients smoothing defense against localized adversarial attacks"
    - "adversarial generative nets neural network attacks on state of the art face recognition"

Connected through hyperedge HE-2 (induced by source paper "on training robust pdf malware classifiers"):
  with members:
    - "robust decision trees against adversarial examples"
    - "scaling provable adversarial defenses"
    - "adversarial attack on graph structured data"
    - "formal security analysis of neural networks using symbolic intervals"
    - "on the effectiveness of interval bound propagation for training verifiably robust models"
    - "a dual approach to scalable verification of deep networks"
    - "cost sensitive robustness against adversarial examples"
    - "training verified learners with learned verifiers"

Connected through hyperedge HE-3 (induced by source paper "an alternative surrogate loss for pgd based adversarial testing"):
  with members:
    - "are labels required for improving adversarial robustness"
    - "theoretically principled trade off between robustness and accuracy"
    - "on evaluating adversarial robustness"
    - "minimally distorted adversarial examples with a fast adaptive boundary attack"
    - "feature denoising for improving adversarial robustness"
    - "robustness via curvature regularization and vice versa"
    - "adversarial logit pairing"
    - "distributionally adversarial attack"

Connected through hyperedge HE-4 (induced by source paper "toward finding the global optimal of adversarial examples"):
  with members:
    - "sign opt a query efficient hard label adversarial attack"
    - "hopskipjumpattack a query efficient decision based attack"
    - "robust decision trees against adversarial examples"
    - "ml loo detecting adversarial examples with feature attribution"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "mixtrain scalable training of formally robust neural networks"
    - "genattack practical black box attacks with gradient free optimization"
    - "black box adversarial attacks with limited queries and information"

Question: Please tell me which class the center node 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)