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: "deep neural networks as 0 1 mixed integer linear programs a feasibility study"
  Abstract: Deep Neural Networks (DNNs) are very popular these days, and are the subject of a very intense investigation. A DNN is made by layers of internal units (or neurons), each of which computes an affine combination of the output of the units in the previous layer, applies a nonlinear operator, and outputs the corresponding value (also known as activation). A commonly-used nonlinear operator is the so-called rectified linear unit (ReLU), whose output is just the maximum between its input value and zero. In this (and other similar cases like max pooling, where the max operation involves more than one input value), one can model the DNN as a 0-1 Mixed Integer Linear Program (0-1 MILP) where the continuous variables correspond to the output values of each unit, and a binary variable is associated with each ReLU to model its yes/no nature. In this paper we discuss the peculiarity of this kind of 0-1 MILP models, and describe an effective bound-tightening technique intended to ease its solution. We also present possible applications of the 0-1 MILP model arising in feature visualization and in the construction of adversarial examples. Preliminary computational results are reported, aimed at investigating (on small DNNs) the computational performance of a state-of-the-art MILP solver when applied to a known test case, namely, hand-written digit recognition.

Connected through hyperedge HE-1 (induced by source paper "randomized smoothing of all shapes and sizes"):
  with members:
    - "curse of dimensionality on randomized smoothing for certifiable robustness"
    - "macer attack free and scalable robust training via maximizing certified radius"
    - "robustness certificates for sparse adversarial attacks by randomized ablation"
    - "certified adversarial robustness via randomized smoothing"
    - "wasserstein smoothing certified robustness against wasserstein adversarial attacks"
    - "a convex relaxation barrier to tight robustness verification of neural networks"
    - "scaling provable adversarial defenses"
    - "on the effectiveness of interval bound propagation for training verifiably robust models"

Connected through hyperedge HE-2 (induced by source paper "injecting domain knowledge in neural networks a controlled experiment on a constrained problem"):
  with members:
    - "integrating learning and reasoning with deep logic models"
    - "semi supervised learning using differentiable reasoning"
    - "deepproblog neural probabilistic logic programming"
    - "machine learning for combinatorial optimization a methodological tour d horizon"
    - "learning explanatory rules from noisy data"
    - "neural combinatorial optimization with reinforcement learning"
    - "logic tensor networks deep learning and logical reasoning from data and knowledge"

Connected through hyperedge HE-3 (induced by source paper "on training robust pdf malware classifiers"):
  with members:
    - "robust decision trees against adversarial examples"
    - "enhancing gradient based attacks with symbolic intervals"
    - "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"

Connected through hyperedge HE-4 (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-5 (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"
    - "evaluating robustness of neural networks with mixed integer programming"
    - "maximum resilience of artificial neural networks"

Connected through hyperedge HE-6 (induced by source paper "provably robust deep learning via adversarially trained smoothed classifiers"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "using pre training can improve model robustness and uncertainty"
    - "a convex relaxation barrier to tight robustness verification of neural networks"
    - "scaling provable adversarial defenses"
    - "adversarial risk and the dangers of evaluating against weak attacks"
    - "on the effectiveness of interval bound propagation for training verifiably robust models"
    - "a dual approach to scalable verification of deep networks"
    - "training verified learners with learned verifiers"

Connected through hyperedge HE-7 (induced by source paper "towards robust locally linear deep networks"):
  with members:
    - "on the convergence of adam and beyond"
    - "scaling provable adversarial defenses"
    - "provable robustness of relu networks via maximization of linear regions"
    - "the cramer distance as a solution to biased wasserstein gradients"
    - "deep defense training dnns with improved adversarial robustness"
    - "on the robustness of interpretability methods"
    - "smoothgrad removing noise by adding noise"
    - "provable defenses against adversarial examples via the convex outer adversarial polytope"

Connected through hyperedge HE-8 (induced by source paper "a convex relaxation barrier to tight robustness verification of neural networks"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "verification of non linear specifications for neural networks"
    - "scaling provable adversarial defenses"
    - "on the effectiveness of interval bound propagation for training verifiably robust models"
    - "training verified learners with learned verifiers"
    - "mixtrain scalable training of formally robust neural networks"
    - "training for faster adversarial robustness verification via inducing relu stability"
    - "semidefinite relaxations for certifying robustness to 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)