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: "concolic testing for deep neural networks"
  Abstract: Concolic testing combines program execution and symbolic analysis to explore the execution paths of a software program. This paper presents the first concolic testing approach for Deep Neural Networks (DNNs). More specifically, we formalise coverage criteria for DNNs that have been studied in the literature, and then develop a coherent method for performing concolic testing to increase test coverage. Our experimental results show the effectiveness of the concolic testing approach in both achieving high coverage and finding adversarial examples.

Connected through hyperedge HE-1 (induced by source paper "importance driven deep learning system testing"):
  with members:
    - "input prioritization for testing neural networks"
    - "deepfault fault localization for deep neural networks"
    - "machine learning testing survey landscapes and horizons"
    - "formal security analysis of neural networks using symbolic intervals"
    - "symbolic execution for deep neural networks"
    - "deepgauge multi granularity testing criteria for deep learning systems"
    - "deepmutation mutation testing of deep learning systems"
    - "safely entering the deep a review of verification and validation for machine learning and a challenge elicitation in the automotive industry"

Connected through hyperedge HE-2 (induced by source paper "raid randomized adversarial input detection for neural networks"):
  with members:
    - "when explainability meets adversarial learning detecting adversarial examples using shap signatures"
    - "are odds really odd bypassing statistical detection of adversarial examples"
    - "deepsmartfuzzer reward guided test generation for deep learning"
    - "deepfault fault localization for deep neural networks"
    - "a new defense against adversarial images turning a weakness into a strength"
    - "the odds are odd a statistical test for detecting adversarial examples"
    - "on evaluating adversarial robustness"
    - "deepgauge multi granularity testing criteria for deep learning systems"

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"
    - "a dual approach to scalable verification of deep networks"
    - "deepgauge multi granularity testing criteria for deep learning systems"
    - "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 "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-5 (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"
    - "a dual approach to scalable verification of deep networks"
    - "stochastic activation pruning for robust adversarial defense"
    - "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-6 (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"
    - "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"
    - "a game based approximate verification of deep neural networks with provable guarantees"

Connected through hyperedge HE-7 (induced by source paper "testing monotonicity of machine learning models"):
  with members:
    - "fairness testing testing software for discrimination"
    - "deepxplore automated whitebox testing of deep learning systems"
    - "why should i trust you explaining the predictions of any classifier"
    - "safety verification of deep neural networks"
    - "towards evaluating the robustness of neural networks"

Connected through hyperedge HE-8 (induced by source paper "testing dnn image classifier for confusion bias errors"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "metric learning for adversarial robustness"
    - "adversarial sample detection for deep neural network through model mutation testing"
    - "noise tolerant fair classification"
    - "scaling provable adversarial defenses"
    - "formal security analysis of neural networks using symbolic intervals"
    - "visual interpretability for deep learning a survey"
    - "feature denoising for improving adversarial robustness"

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)