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: "maximum resilience of artificial neural networks"
  Abstract: The deployment of Artificial Neural Networks (ANNs) in safety-critical applications poses a number of new verification and certification challenges. In particular, for ANN-enabled self-driving vehicles it is important to establish properties about the resilience of ANNs to noisy or even maliciously manipulated sensory input. We are addressing these challenges by defining resilience properties of ANN-based classifiers as the maximal amount of input or sensor perturbation which is still tolerated. This problem of computing maximal perturbation bounds for ANNs is then reduced to solving mixed integer optimization problems (MIP). A number of MIP encoding heuristics are developed for drastically reducing MIP-solver runtimes, and using parallelization of MIP-solvers results in an almost linear speed-up in the number (up to a certain limit) of computing cores in our experiments. We demonstrate the effectiveness and scalability of our approach by means of computing maximal resilience bounds for a number of ANN benchmark sets ranging from typical image recognition scenarios to the autonomous maneuvering of robots.

Connected through hyperedge HE-1 (induced by source paper "lossless compression of deep neural networks"):
  with members:
    - "mipaal mixed integer program as a layer"
    - "caql continuous action q learning"
    - "function approximation by deep networks"
    - "equivalent and approximate transformations of deep neural networks"
    - "differentiable convex optimization layers"
    - "accelerating primal solution findings for mixed integer programs based on solution prediction"
    - "reinforcement learning for integer programming learning to cut"
    - "machine learning for combinatorial optimization a methodological tour d horizon"

Connected through hyperedge HE-2 (induced by source paper "certified robustness of community detection against adversarial structural perturbation via randomized smoothing"):
  with members:
    - "adversarial examples on graph data deep insights into attack and defense"
    - "topology attack and defense for graph neural networks an optimization perspective"
    - "tight certificates of adversarial robustness for randomly smoothed classifiers"
    - "certified adversarial robustness via randomized smoothing"
    - "certified robustness for top k predictions against adversarial perturbations via randomized smoothing"
    - "adversarial attacks on graph neural networks via meta learning"
    - "certifiable robustness to graph perturbations"
    - "attacking graph based classification via manipulating the graph structure"

Connected through hyperedge HE-3 (induced by source paper "exploiting verified neural networks via floating point numerical error"):
  with members:
    - "on adaptive attacks to adversarial example defenses"
    - "correctness verification of neural networks"
    - "training verified learners with learned verifiers"
    - "adversarial logit pairing"
    - "training for faster adversarial robustness verification via inducing relu stability"
    - "semidefinite relaxations for certifying robustness to adversarial examples"
    - "efficient neural network robustness certification with general activation functions"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"

Connected through hyperedge HE-4 (induced by source paper "scalable quantitative verification for deep neural networks"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "quantitative verification of neural networks and its security applications"
    - "on evaluating adversarial robustness"
    - "a dual approach to scalable verification of deep networks"
    - "stochastic activation pruning for robust adversarial defense"
    - "concolic testing for deep neural networks"
    - "certified defenses against adversarial examples"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"

Connected through hyperedge HE-5 (induced by source paper "certified robustness for top k predictions against adversarial perturbations via randomized smoothing"):
  with members:
    - "tight certificates of adversarial robustness for randomly smoothed classifiers"
    - "certified adversarial robustness via randomized smoothing"
    - "theoretical evidence for adversarial robustness through randomization"
    - "adv bnn improved adversarial defense through robust bayesian neural network"
    - "scaling provable adversarial defenses"
    - "on the effectiveness of interval bound propagation for training verifiably robust models"
    - "sorting out lipschitz function approximation"
    - "a dual approach to scalable verification of deep networks"

Connected through hyperedge HE-6 (induced by source paper "nattack learning the distributions of adversarial examples for an improved black box attack on deep neural networks"):
  with members:
    - "a direct approach to robust deep learning using adversarial networks"
    - "interpreting adversarial examples by activation promotion and suppression"
    - "adv bnn improved adversarial defense through robust bayesian neural network"
    - "stochastic activation pruning for robust adversarial defense"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "lipschitz margin training scalable certification of perturbation invariance for deep neural networks"
    - "how local is the local diversity reinforcing sequential determinantal point processes with dynamic ground sets for supervised video summarization"
    - "on the robustness of the cvpr 2018 white box adversarial example defenses"

Connected through hyperedge HE-7 (induced by source paper "equivalent and approximate transformations of deep neural networks"):
  with members:
    - "quantized densely connected u nets for efficient landmark localization"
    - "learning strict identity mappings in deep residual networks"
    - "training for faster adversarial robustness verification via inducing relu stability"
    - "resnet with one neuron hidden layers is a universal approximator"
    - "empirical bounds on linear regions of deep rectifier networks"
    - "deep networks with probabilistic gates"
    - "deep neural networks as 0 1 mixed integer linear programs a feasibility study"
    - "evaluating robustness of neural networks with mixed integer programming"

Connected through hyperedge HE-8 (induced by source paper "preventing gradient attenuation in lipschitz constrained convolutional networks"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "universal lipschitz approximation in bounded depth neural networks"
    - "provably robust deep learning via adversarially trained smoothed classifiers"
    - "provable robustness against all adversarial l_p perturbations for p geq 1"
    - "on evaluating adversarial robustness"
    - "scaling provable adversarial defenses"
    - "sorting out lipschitz function approximation"
    - "limitations of the lipschitz constant as a defense against adversarial examples"

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)