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: "global robustness evaluation of deep neural networks with provable guarantees for l0 norm"
  Abstract: Deployment of deep neural networks (DNNs) in safety- or security-critical systems requires provable guarantees on their correct behaviour. A common requirement is robustness to adversarial perturbations in a neighbourhood around an input. In this paper we focus on the $L_0$ norm and aim to compute, for a trained DNN and an input, the maximal radius of a safe norm ball around the input within which there are no adversarial examples. Then we define global robustness as an expectation of the maximal safe radius over a test data set. We first show that the problem is NP-hard, and then propose an approximate approach to iteratively compute lower and upper bounds on the network's robustness. The approach is \emph{anytime}, i.e., it returns intermediate bounds and robustness estimates that are gradually, but strictly, improved as the computation proceeds; \emph{tensor-based}, i.e., the computation is conducted over a set of inputs simultaneously, instead of one by one, to enable efficient GPU computation; and has \emph{provable guarantees}, i.e., both the bounds and the robustness estimates can converge to their optimal values. Finally, we demonstrate the utility of the proposed approach in practice to compute tight bounds by applying and adapting the anytime algorithm to a set of challenging problems, including global robustness evaluation, competitive $L_0$ attacks, test case generation for DNNs, and local robustness evaluation on large-scale ImageNet DNNs. We release the code of all case studies via GitHub.

Connected through hyperedge HE-1 (induced by source paper "generating realistic unrestricted adversarial inputs using dual objective gan training"):
  with members:
    - "a research agenda dynamic models to defend against correlated attacks"
    - "algorithms for verifying deep neural networks"
    - "adversarial logit pairing"
    - "concolic testing for deep neural networks"
    - "towards the first adversarially robust neural network model on mnist"
    - "are adversarial examples inevitable"
    - "provable robustness of relu networks via maximization of linear regions"
    - "exploring the landscape of spatial robustness"

Connected through hyperedge HE-2 (induced by source paper "testing deep neural networks"):
  with members:
    - "adversarial sample detection for deep neural network through model mutation testing"
    - "a formalization of robustness for deep neural networks"
    - "deepgauge comprehensive and multi granularity testing criteria for gauging the robustness of deep learning systems"
    - "formal security analysis of neural networks using symbolic intervals"
    - "formal verification of neural network controlled autonomous systems"
    - "symbolic execution for deep neural networks"
    - "identifying implementation bugs in machine learning based image classifiers using metamorphic testing"
    - "deepmutation mutation testing of deep learning systems"

Connected through hyperedge HE-3 (induced by source paper "formal verification of neural network controlled autonomous systems"):
  with members:
    - "adversarial sample detection for deep neural network through model mutation testing"
    - "deepgauge comprehensive and multi granularity testing criteria for gauging the robustness of deep learning systems"
    - "automated verification of neural networks advances challenges and perspectives"
    - "robust deep reinforcement learning for security and safety in autonomous vehicle systems"
    - "agi safety literature review"
    - "deepgauge multi granularity testing criteria for deep learning systems"
    - "deepmutation mutation testing of deep learning systems"
    - "concolic testing for deep neural networks"

Connected through hyperedge HE-4 (induced by source paper "a game based approximate verification of deep neural networks with provable guarantees"):
  with members:
    - "scaling provable adversarial defenses"
    - "concolic testing for deep neural networks"
    - "reachability analysis of deep neural networks with provable guarantees"
    - "efficient neural network robustness certification with general activation functions"
    - "evaluating robustness of neural networks with mixed integer programming"
    - "provable defenses against adversarial examples via the convex outer adversarial polytope"
    - "feature guided black box safety testing of deep neural networks"
    - "a unified approach to interpreting model predictions"

Connected through hyperedge HE-5 (induced by source paper "reachability analysis of deep neural networks with provable guarantees"):
  with members:
    - "concolic testing for deep neural networks"
    - "provable defenses against adversarial examples via the convex outer adversarial polytope"
    - "an approach to reachability analysis for feed forward relu neural networks"
    - "maximum resilience of artificial neural networks"
    - "output range analysis for deep neural networks"
    - "formal verification of piece wise linear feed forward neural networks"
    - "feature guided black box safety testing of deep neural networks"
    - "output reachable set estimation and verification for multi layer neural networks"

Connected through hyperedge HE-6 (induced by source paper "concolic testing for deep neural networks"):
  with members:
    - "deepgauge comprehensive and multi granularity testing criteria for gauging the robustness of deep learning systems"
    - "deepgauge multi granularity testing criteria for deep learning systems"
    - "reachability analysis of deep neural networks with provable guarantees"
    - "testing deep neural networks"
    - "a game based approximate verification of deep neural networks with provable guarantees"
    - "lipschitz properties for deep convolutional networks"
    - "deepxplore automated whitebox testing of deep learning systems"
    - "feature guided black box safety testing of deep neural networks"

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)