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: "a dual approach to scalable verification of deep networks"
  Abstract: This paper addresses the problem of formally verifying desirable properties of neural networks, i.e., obtaining provable guarantees that the outputs of the neural network will always behave in a certain way for a given class of inputs. Most previous work on this topic was limited in its applicability by the size of the network, network architecture and the complexity of properties to be verified. In contrast, our framework applies to much more general class of activation functions and specifications on neural network inputs and outputs. We formulate verification as an optimization problem and solve a Lagrangian relaxation of the optimization problem to obtain an upper bound on the verification objective. Our approach is anytime, i.e. it can be stopped at any time and a valid bound on the objective can be obtained. We develop specialized verification algorithms with provable tightness guarantees under special assumptions and demonstrate the practical significance of our general verification approach on a variety of verification tasks.

Connected through hyperedge HE-1 (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-2 (induced by source paper "resilient cyberphysical systems and their application drivers a technology roadmap"):
  with members:
    - "traditional and heavy tailed self regularization in neural network models"
    - "numerical verification of affine systems with up to a billion dimensions"
    - "juliareach a toolbox for set based reachability"
    - "heavy tailed universality predicts trends in test accuracies for very large pre trained deep neural networks"
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "formal security analysis of neural networks using symbolic intervals"
    - "verisig verifying safety properties of hybrid systems with neural network controllers"
    - "semidefinite relaxations for certifying robustness to adversarial examples"

Connected through hyperedge HE-3 (induced by source paper "reludiff differential verification of deep neural networks"):
  with members:
    - "towards compact and robust deep neural networks"
    - "deep neural network compression for aircraft collision avoidance systems"
    - "formal security analysis of neural networks using symbolic intervals"
    - "deepgauge multi granularity testing criteria for deep learning systems"
    - "concolic testing for deep neural networks"
    - "reachability analysis of deep neural networks with provable guarantees"
    - "efficient formal safety analysis of neural networks"
    - "certified defenses against 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"
    - "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"
    - "evaluating robustness of neural networks with mixed integer programming"

Connected through hyperedge HE-5 (induced by source paper "black box certification with randomized smoothing a functional optimization based framework"):
  with members:
    - "provable certificates for adversarial examples fitting a ball in the union of polytopes"
    - "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"
    - "adversarial logit pairing"

Connected through hyperedge HE-6 (induced by source paper "taking care of the discretization problem a black box adversarial image attack in discrete integer domain"):
  with members:
    - "towards improved testing for deep learning"
    - "one pixel attack for fooling deep neural networks"
    - "procedural noise adversarial examples for black box attacks on deep convolutional networks"
    - "on the design of black box adversarial examples by leveraging gradient free optimization and operator splitting method"
    - "autozoom autoencoder based zeroth order optimization method for attacking black box neural networks"
    - "spatially transformed adversarial examples"
    - "formal security analysis of neural networks using symbolic intervals"
    - "commandersong a systematic approach for practical adversarial voice recognition"

Connected through hyperedge HE-7 (induced by source paper "fine grained synthesis of unrestricted adversarial examples"):
  with members:
    - "neural puppet generative layered cartoon characters"
    - "certified adversarial robustness via randomized smoothing"
    - "regional homogeneity towards learning transferable universal adversarial perturbations against defenses"
    - "spatially transformed adversarial examples"
    - "adversarial risk and the dangers of evaluating against weak attacks"
    - "adversarial patch"
    - "feature denoising for improving adversarial robustness"
    - "adef an iterative algorithm to construct adversarial deformations"

Connected through hyperedge HE-8 (induced by source paper "quantitative verification of neural networks and its security applications"):
  with members:
    - "phase transition behavior of cardinality and xor constraints"
    - "adversarial examples are a natural consequence of test error in noise"
    - "strip a defence against trojan attacks on deep neural networks"
    - "the curse of concentration in robust learning evasion and poisoning attacks from concentration of measure"
    - "formal security analysis of neural networks using symbolic intervals"
    - "the secret sharer measuring unintended neural network memorization extracting secrets"
    - "adversarial spheres"
    - "reachability analysis of deep neural networks with provable guarantees"

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)