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: "provable certificates for adversarial examples fitting a ball in the union of polytopes"
  Abstract: We propose a novel method for computing exact pointwise robustness of deep neural networks for a number of $\ell_p$ norms. Our algorithm, GeoCert, finds the largest $\ell_p$ ball centered at an input point $x_0$, within which the output class of a given neural network with ReLU nonlinearities remains unchanged. We relate the problem of computing pointwise robustness of these networks to that of growing a norm ball inside a non-convex polytope. This is a challenging problem in general, as we discuss; however, we prove a useful structural result about the geometry of the piecewise linear components of ReLU networks. This result allows for an efficient convex decomposition of the problem. Specifically we show that if polytopes satisfy a technical condition that we call being 'perfectly-glued', then we can find the largest ball inside their union in polynomial time. Our method is efficient and can certify pointwise robustness for any norm where p is greater or equal to 1.

Connected through hyperedge HE-1 (induced by source paper "robustness for non parametric classification a generic attack and defense"):
  with members:
    - "robust decision trees against adversarial examples"
    - "on the robustness of deep k nearest neighbors"
    - "theoretically principled trade off between robustness and accuracy"
    - "provably robust boosted decision stumps and trees against adversarial attacks"
    - "on evaluating adversarial robustness"
    - "adversarial training with voronoi constraints"
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "training for faster adversarial robustness verification via inducing relu stability"

Connected through hyperedge HE-2 (induced by source paper "black box certification with randomized smoothing a functional optimization based framework"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "certified robustness for top k predictions against adversarial perturbations via randomized smoothing"
    - "adversarially robust generalization just requires more unlabeled data"
    - "improving neural language modeling via adversarial training"
    - "provably robust deep learning via adversarially trained smoothed classifiers"
    - "defense against adversarial attacks using feature scattering based adversarial training"
    - "a dual approach to scalable verification of deep networks"
    - "adversarial logit pairing"

Connected through hyperedge HE-3 (induced by source paper "fast geometric projections for local robustness certification"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "formal security analysis of neural networks using symbolic intervals"
    - "efficient neural network robustness certification with general activation functions"
    - "formal verification of piece wise linear feed forward neural networks"
    - "reluplex an efficient smt solver for verifying deep neural networks"
    - "the limitations of deep learning in adversarial settings"
    - "intriguing properties of neural networks"

Connected through hyperedge HE-4 (induced by source paper "efficient and accurate estimation of lipschitz constants for deep neural networks"):
  with members:
    - "scaling provable adversarial defenses"
    - "on lipschitz bounds of general convolutional neural networks"
    - "stability certified reinforcement learning a control theoretic perspective"
    - "limitations of the lipschitz constant as a defense against adversarial examples"
    - "lipschitz margin training scalable certification of perturbation invariance for deep neural networks"
    - "l2 nonexpansive neural networks"
    - "semidefinite relaxations for certifying robustness to adversarial examples"
    - "reachability analysis of deep neural networks with provable guarantees"

Connected through hyperedge HE-5 (induced by source paper "computing linear restrictions of neural networks"):
  with members:
    - "deep neural network compression for aircraft collision avoidance systems"
    - "how important is a neuron"
    - "formal security analysis of neural networks using symbolic intervals"
    - "reachable set estimation and safety verification for piecewise linear systems with neural network controllers"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "reachable set computation and safety verification for neural networks with relu activations"
    - "network dissection quantifying interpretability of deep visual representations"
    - "formal verification of piece wise linear feed forward neural networks"

Connected through hyperedge HE-6 (induced by source paper "adversarial examples for non parametric methods attacks defenses and large sample limits"):
  with members:
    - "robustness certificates against adversarial examples for relu networks"
    - "robust decision trees against adversarial examples"
    - "on the robustness of deep k nearest neighbors"
    - "defense against adversarial images using web scale nearest neighbor search"
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "formal verification of cnn based perception systems"
    - "improving the generalization of adversarial training with domain adaptation"
    - "provable robustness of relu networks via maximization of linear regions"

Connected through hyperedge HE-7 (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:
    - "disentangling adversarial robustness and generalization"
    - "a randomized gradient free attack on relu networks"
    - "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)