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: "theoretical evidence for adversarial robustness through randomization"
  Abstract: This paper investigates the theory of robustness against adversarial attacks. It focuses on the family of randomization techniques that consist in injecting noise in the network at inference time. These techniques have proven effective in many contexts, but lack theoretical arguments. We close this gap by presenting a theoretical analysis of these approaches, hence explaining why they perform well in practice. More precisely, we make two new contributions. The first one relates the randomization rate to robustness to adversarial attacks. This result applies for the general family of exponential distributions, and thus extends and unifies the previous approaches. The second contribution consists in devising a new upper bound on the adversarial generalization gap of randomized neural networks. We support our theoretical claims with a set of experiments.

Connected through hyperedge HE-1 (induced by source paper "randomization matters how to defend against strong adversarial attacks"):
  with members:
    - "on the hardness of robust classification"
    - "certified adversarial robustness via randomized smoothing"
    - "lower bounds on adversarial robustness from optimal transport"
    - "robust attacks against multiple classifiers"
    - "adversarial risk via optimal transport and optimal couplings"
    - "on evaluating adversarial robustness"
    - "resnets ensemble via the feynman kac formalism to improve natural and robust accuracies"
    - "stochastic activation pruning for robust adversarial defense"

Connected through hyperedge HE-2 (induced by source paper "towards rapid and robust adversarial training with one step attacks"):
  with members:
    - "fast is better than free revisiting adversarial training"
    - "adversarial training and robustness for multiple perturbations"
    - "robust neural networks using randomized adversarial training"
    - "on evaluating adversarial robustness"
    - "towards the first adversarially robust neural network model on mnist"
    - "mixed precision training"
    - "towards robust neural networks via random self ensemble"
    - "mitigating adversarial effects through randomization"

Connected through hyperedge HE-3 (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"
    - "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"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"

Connected through hyperedge HE-4 (induced by source paper "are perceptually aligned gradients a general property of robust classifiers"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "certifiably robust interpretation in deep learning"
    - "defending against whitebox adversarial attacks via randomized discretization"
    - "a convex relaxation barrier to tight robustness verification of neural networks"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "towards robust neural networks via random self ensemble"
    - "mitigating evasion attacks to deep neural networks via region based classification"
    - "improving the robustness of deep neural networks via stability training"

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)