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 game based approximate verification of deep neural networks with provable guarantees"
  Abstract: Despite the improved accuracy of deep neural networks, the discovery of adversarial examples has raised serious safety concerns. In this paper, we study two variants of pointwise robustness, the maximum safe radius problem, which for a given input sample computes the minimum distance to an adversarial example, and the feature robustness problem, which aims to quantify the robustness of individual features to adversarial perturbations. We demonstrate that, under the assumption of Lipschitz continuity, both problems can be approximated using finite optimisation by discretising the input space, and the approximation has provable guarantees, i.e., the error is bounded. We then show that the resulting optimisation problems can be reduced to the solution of two-player turn-based games, where the first player selects features and the second perturbs the image within the feature. While the second player aims to minimise the distance to an adversarial example, depending on the optimisation objective the first player can be cooperative or competitive. We employ an anytime approach to solve the games, in the sense of approximating the value of a game by monotonically improving its upper and lower bounds. The Monte Carlo tree search algorithm is applied to compute upper bounds for both games, and the Admissible A* and the Alpha-Beta Pruning algorithms are, respectively, used to compute lower bounds for the maximum safety radius and feature robustness games. When working on the upper bound of the maximum safe radius problem, our tool demonstrates competitive performance against existing adversarial example crafting algorithms. Furthermore, we show how our framework can be deployed to evaluate pointwise robustness of neural networks in safety-critical applications such as traffic sign recognition in self-driving cars.

Connected through hyperedge HE-1 (induced by source paper "reliability validation of learning enabled vehicle tracking"):
  with members:
    - "test metrics for recurrent neural networks"
    - "reasoning about cognitive trust in stochastic multiagent systems"
    - "adversarial sample detection for deep neural network through model mutation testing"
    - "detecting and tracking small moving objects in wide area motion imagery wami using convolutional neural networks cnns"
    - "deepgauge comprehensive and multi granularity testing criteria for gauging the robustness of deep learning 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-2 (induced by source paper "a safety framework for critical systems utilising deep neural networks"):
  with members:
    - "assuring the machine learning lifecycle desiderata methods and challenges"
    - "assessing the safety and reliability of autonomous vehicles from road testing"
    - "deepgauge comprehensive and multi granularity testing criteria for gauging the robustness of deep learning systems"
    - "gradient descent finds global minima of deep neural networks"
    - "concolic testing for deep neural networks"
    - "reachability analysis of deep neural networks with provable guarantees"
    - "using machine learning safely in automotive software an assessment and adaption of software process requirements in iso 26262"
    - "a survey of safety and trustworthiness of deep neural networks"

Connected through hyperedge HE-3 (induced by source paper "testing and verification of neural network based safety critical control software a systematic literature review"):
  with members:
    - "visual analytics in deep learning an interrogative survey for the next frontiers"
    - "local rule based explanations of black box decision systems"
    - "deepgauge multi granularity testing criteria for deep learning systems"
    - "experimental resilience assessment of an open source driving agent"
    - "interpreting deep classifier by visual distillation of dark knowledge"
    - "dlfuzz differential fuzzing testing of deep learning systems"
    - "software engineering challenges of deep learning"
    - "feature squeezing detecting adversarial examples in deep neural networks"

Connected through hyperedge HE-4 (induced by source paper "deepfault fault localization for deep neural networks"):
  with members:
    - "symbolic execution for deep neural networks"
    - "deepgauge multi granularity testing criteria for deep learning systems"
    - "deepmutation mutation testing of deep learning systems"
    - "concolic testing for deep neural networks"
    - "guiding deep learning system testing using surprise adequacy"
    - "dlfuzz differential fuzzing testing of deep learning systems"
    - "combinatorial testing for deep learning systems"
    - "testing deep neural networks"

Connected through hyperedge HE-5 (induced by source paper "formal methods and software engineering for dl security safety and productivity for dl systems development"):
  with members:
    - "relay a new ir for machine learning frameworks"
    - "nn dependability kit engineering neural networks for safety critical systems"
    - "automated verification of neural networks advances challenges and perspectives"
    - "reachability analysis of deep neural networks with provable guarantees"
    - "no need to worry about adversarial examples in object detection in autonomous vehicles"
    - "towards proving the adversarial robustness of deep neural networks"
    - "output range analysis for deep neural networks"
    - "formal verification of piece wise linear feed forward neural networks"

Connected through hyperedge HE-6 (induced by source paper "robustness of 3d deep learning in an adversarial setting"):
  with members:
    - "statistical guarantees for the robustness of bayesian neural networks"
    - "dynamic graph cnn for learning on point clouds"
    - "reachability analysis of deep neural networks with provable guarantees"
    - "voxelnet end to end learning for point cloud based 3d object detection"
    - "frustum pointnets for 3d object detection from rgb d data"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "feature guided black box safety testing of deep neural networks"
    - "reluplex an efficient smt solver for verifying deep neural networks"

Connected through hyperedge HE-7 (induced by source paper "statistical guarantees for the robustness of bayesian neural networks"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "robustness guarantees for bayesian inference with gaussian processes"
    - "verification of deep probabilistic models"
    - "the limitations of model uncertainty in adversarial settings"
    - "reachability analysis of deep neural networks with provable guarantees"
    - "feature guided black box safety testing of deep neural networks"
    - "reluplex an efficient smt solver for verifying deep neural networks"

Connected through hyperedge HE-8 (induced by source paper "a survey of safety and trustworthiness of deep neural networks"):
  with members:
    - "analyzing deep neural networks with symbolic propagation towards higher precision and faster verification"
    - "deepgauge comprehensive and multi granularity testing criteria for gauging the robustness of deep learning systems"
    - "nn dependability kit engineering neural networks for safety critical systems"
    - "formal security analysis of neural networks using symbolic intervals"
    - "a dual approach to scalable verification of deep networks"
    - "towards dependability metrics for neural networks"
    - "deepmutation mutation testing of deep learning systems"
    - "concolic testing for 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)