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: "a randomized gradient free attack on relu networks"
  Abstract: It has recently been shown that neural networks but also other classifiers are vulnerable to so called adversarial attacks e.g. in object recognition an almost non-perceivable change of the image changes the decision of the classifier. Relatively fast heuristics have been proposed to produce these adversarial inputs but the problem of finding the optimal adversarial input, that is with the minimal change of the input, is NP-hard. While methods based on mixed-integer optimization which find the optimal adversarial input have been developed, they do not scale to large networks. Currently, the attack scheme proposed by Carlini and Wagner is considered to produce the best adversarial inputs. In this paper we propose a new attack scheme for the class of ReLU networks based on a direct optimization on the resulting linear regions. In our experimental validation we improve in all except one experiment out of 18 over the Carlini-Wagner attack with a relative improvement of up to 9\%. As our approach is based on the geometrical structure of ReLU networks, it is less susceptible to defences targeting their functional properties.

Connected through hyperedge HE-1 (induced by source paper "provable robustness against all adversarial l_p perturbations for p geq 1"):
  with members:
    - "adversarial training and robustness for multiple perturbations"
    - "transfer of adversarial robustness between perturbation types"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "scaling up the randomized gradient free adversarial attack reveals overestimation of robustness using established attacks"
    - "minimally distorted adversarial examples with a fast adaptive boundary attack"
    - "scaling provable adversarial defenses"
    - "on the effectiveness of interval bound propagation for training verifiably robust models"
    - "adversarial patch"

Connected through hyperedge HE-2 (induced by source paper "scaling up the randomized gradient free adversarial attack reveals overestimation of robustness using established attacks"):
  with members:
    - "scaling provable adversarial defenses"
    - "logit pairing methods can fool gradient based attacks"
    - "towards the first adversarially robust neural network model on mnist"
    - "provable robustness of relu networks via maximization of linear regions"
    - "certified defenses against adversarial examples"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "evaluating robustness of neural networks with mixed integer programming"
    - "adversarial examples are not easily detected bypassing ten detection methods"

Connected through hyperedge HE-3 (induced by source paper "analysis of confident classifiers for out of distribution detection"):
  with members:
    - "improving reconstruction autoencoder out of distribution detection with mahalanobis distance"
    - "deep anomaly detection with outlier exposure"
    - "generative probabilistic novelty detection with adversarial autoencoders"
    - "why relu networks yield high confidence predictions far away from the training data and how to mitigate the problem"
    - "denoising autoencoders for overgeneralization in neural networks"
    - "safer classification by synthesis"
    - "deep neural networks are easily fooled high confidence predictions for unrecognizable images"

Connected through hyperedge HE-4 (induced by source paper "provable robustness of relu networks via maximization of linear regions"):
  with members:
    - "scaling up the randomized gradient free adversarial attack reveals overestimation of robustness using established attacks"
    - "scaling provable adversarial defenses"
    - "logit pairing methods can fool gradient based attacks"
    - "training for faster adversarial robustness verification via inducing relu stability"
    - "certified defenses against adversarial examples"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "evaluating robustness of neural networks with mixed integer programming"
    - "adversarial examples are not easily detected bypassing ten detection methods"

Connected through hyperedge HE-5 (induced by source paper "why relu networks yield high confidence predictions far away from the training data and how to mitigate the problem"):
  with members:
    - "provable certificates for adversarial examples fitting a ball in the union of polytopes"
    - "disentangling adversarial robustness and generalization"
    - "deep anomaly detection with outlier exposure"
    - "certified defenses against adversarial examples"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "enhancing the reliability of out of distribution image detection in neural networks"
    - "on calibration of modern neural networks"
    - "adversarial examples are not easily detected bypassing ten detection methods"

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)