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: "semidefinite relaxations for certifying robustness to adversarial examples"
  Abstract: Despite their impressive performance on diverse tasks, neural networks fail catastrophically in the presence of adversarial inputs---imperceptibly but adversarially perturbed versions of natural inputs. We have witnessed an arms race between defenders who attempt to train robust networks and attackers who try to construct adversarial examples. One promise of ending the arms race is developing certified defenses, ones which are provably robust against all attackers in some family. These certified defenses are based on convex relaxations which construct an upper bound on the worst case loss over all attackers in the family. Previous relaxations are loose on networks that are not trained against the respective relaxation. In this paper, we propose a new semidefinite relaxation for certifying robustness that applies to arbitrary ReLU networks. We show that our proposed relaxation is tighter than previous relaxations and produces meaningful robustness guarantees on three different "foreign networks" whose training objectives are agnostic to our proposed relaxation.

Connected through hyperedge HE-1 (induced by source paper "overfitting in adversarially robust deep learning"):
  with members:
    - "fast is better than free revisiting adversarial training"
    - "adversarial training and robustness for multiple perturbations"
    - "certified adversarial robustness via randomized smoothing"
    - "deep double descent where bigger models and more data hurt"
    - "wasserstein adversarial examples via projected sinkhorn iterations"
    - "are labels required for improving adversarial robustness"
    - "adversarially robust generalization just requires more unlabeled data"
    - "me net towards effective adversarial robustness with matrix estimation"

Connected through hyperedge HE-2 (induced by source paper "robust deep reinforcement learning against adversarial perturbations on observations"):
  with members:
    - "automatic perturbation analysis on general computational graphs"
    - "certified adversarial robustness for deep reinforcement learning"
    - "online robustness training for deep reinforcement learning"
    - "action robust reinforcement learning and applications in continuous control"
    - "towards stable and efficient training of verifiably robust neural networks"
    - "a convex relaxation barrier to tight robustness verification of neural networks"
    - "robust reinforcement learning for continuous control with model misspecification"
    - "scaling provable adversarial defenses"

Connected through hyperedge HE-3 (induced by source paper "black box smoothing a provable defense for pretrained classifiers"):
  with members:
    - "macer attack free and scalable robust training via maximizing certified radius"
    - "tight certificates of adversarial robustness for randomly smoothed classifiers"
    - "certified adversarial robustness via randomized smoothing"
    - "simple black box adversarial attacks"
    - "a convex relaxation barrier to tight robustness verification of neural networks"
    - "scaling provable adversarial defenses"
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "adversarial risk and the dangers of evaluating against weak attacks"

Connected through hyperedge HE-4 (induced by source paper "robustness verification for transformers"):
  with members:
    - "correctness verification of neural networks"
    - "adversarial examples with difficult common words for paraphrase identification"
    - "certified robustness to adversarial word substitutions"
    - "verifying aircraft collision avoidance neural networks through linear approximations of safe regions"
    - "art abstraction refinement guided training for provably correct neural networks"
    - "semi supervised graph classification a hierarchical graph perspective"
    - "achieving verified robustness to symbol substitutions via interval bound propagation"
    - "verification of non linear specifications for neural networks"

Connected through hyperedge HE-5 (induced by source paper "fast is better than free revisiting adversarial training"):
  with members:
    - "adversarial training and robustness for multiple perturbations"
    - "certified adversarial robustness via randomized smoothing"
    - "me net towards effective adversarial robustness with matrix estimation"
    - "provably robust deep learning via adversarially trained smoothed classifiers"
    - "adversarial training for free"
    - "adversarial robustness against the union of multiple perturbation models"
    - "on evaluating adversarial robustness"
    - "scaling provable adversarial defenses"

Connected through hyperedge HE-6 (induced by source paper "automatic perturbation analysis on general computational graphs"):
  with members:
    - "robustness verification for transformers"
    - "topology attack and defense for graph neural networks an optimization perspective"
    - "fastened crown tightened neural network robustness certificates"
    - "certified robustness to adversarial word substitutions"
    - "verifying aircraft collision avoidance neural networks through linear approximations of safe regions"
    - "achieving verified robustness to symbol substitutions via interval bound propagation"
    - "popqorn quantifying robustness of recurrent neural networks"
    - "asymmetric valleys beyond sharp and flat local minima"

Connected through hyperedge HE-7 (induced by source paper "on the security of randomized defenses against adversarial samples"):
  with members:
    - "breaking certified defenses semantic adversarial examples with spoofed robustness certificates"
    - "certified adversarial robustness via randomized smoothing"
    - "certified robustness for top k predictions against adversarial perturbations via randomized smoothing"
    - "a simple explanation for the existence of adversarial examples with small hamming distance"
    - "on evaluating adversarial robustness"
    - "training verified learners with learned verifiers"
    - "breaking transferability of adversarial samples with randomness"
    - "are adversarial examples inevitable"

Connected through hyperedge HE-8 (induced by source paper "more data can expand the generalization gap between adversarially robust and standard models"):
  with members:
    - "adversarially robust learning could leverage computational hardness"
    - "lower bounds for adversarially robust pac learning"
    - "adversarial robustness may be at odds with simplicity"
    - "certified adversarial robustness via randomized smoothing"
    - "lower bounds on adversarial robustness from optimal transport"
    - "vc classes are adversarially robustly learnable but only improperly"
    - "theoretically principled trade off between robustness and accuracy"
    - "adversarial training can hurt generalization"

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)