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: "algorithms for verifying deep neural networks"
  Abstract: Deep neural networks are widely used for nonlinear function approximation with applications ranging from computer vision to control. Although these networks involve the composition of simple arithmetic operations, it can be very challenging to verify whether a particular network satisfies certain input-output properties. This article surveys methods that have emerged recently for soundly verifying such properties. These methods borrow insights from reachability analysis, optimization, and search. We discuss fundamental differences and connections between existing algorithms. In addition, we provide pedagogical implementations of existing methods and compare them on a set of benchmark problems.

Connected through hyperedge HE-1 (induced by source paper "parot a practical framework for robust deep neural network training"):
  with members:
    - "a provable defense for deep residual networks"
    - "a convex relaxation barrier to tight robustness verification of neural networks"
    - "scaling provable adversarial defenses"
    - "symbolic execution for deep neural networks"
    - "mixtrain scalable training of formally robust neural networks"
    - "using machine learning safely in automotive software an assessment and adaption of software process requirements in iso 26262"
    - "with friends like these who needs adversaries"
    - "certified defenses against adversarial examples"

Connected through hyperedge HE-2 (induced by source paper "semantic adversarial perturbations using learnt representations"):
  with members:
    - "semanticadv generating adversarial examples via attribute conditional image editing"
    - "generating semantic adversarial examples with differentiable rendering"
    - "testing robustness against unforeseen adversaries"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "adversarial attacks and defenses in images graphs and text a review"
    - "adaptive generation of unrestricted adversarial inputs"
    - "semantic adversarial examples"
    - "gan dissection visualizing and understanding generative adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "safety considerations in deep control policies with safety barrier certificates under uncertainty"):
  with members:
    - "control barrier functions theory and applications"
    - "safe reinforcement learning with scene decomposition for navigating complex urban environments"
    - "barrier certified adaptive reinforcement learning with applications to brushbot navigation"
    - "an efficient reachability based framework for provably safe autonomous navigation in unknown environments"
    - "temporal logic guided safe reinforcement learning using control barrier functions"
    - "worst cases policy gradients"
    - "end to end safe reinforcement learning through barrier functions for safety critical continuous control tasks"
    - "sampling based motion planning via control barrier functions"

Connected through hyperedge HE-4 (induced by source paper "validation of image based neural network controllers through adaptive stress testing"):
  with members:
    - "efficient autonomy validation in simulation with adaptive stress testing"
    - "verisig verifying safety properties of hybrid systems with neural network controllers"
    - "reachability analysis and safety verification for neural network control systems"
    - "efficient formal safety analysis of neural networks"
    - "an approach to reachability analysis for feed forward relu neural networks"
    - "towards proving the adversarial robustness of deep neural networks"
    - "reluplex an efficient smt solver for verifying deep neural networks"
    - "learning quadrotor dynamics using neural network for flight control"

Connected through hyperedge HE-5 (induced by source paper "safety considerations in deep control policies with probabilistic safety barrier certificates"):
  with members:
    - "safe reinforcement learning with scene decomposition for navigating complex urban environments"
    - "worst cases policy gradients"
    - "learning controls using cross modal representations bridging simulation and reality for drone racing"
    - "oil observational imitation learning"
    - "fastrack a modular framework for fast and guaranteed safe motion planning"
    - "towards optimally decentralized multi robot collision avoidance via deep reinforcement learning"
    - "reluplex an efficient smt solver for verifying deep neural networks"

Connected through hyperedge HE-6 (induced by source paper "deepsearch simple and effective blackbox fuzzing of deep neural networks"):
  with members:
    - "towards improved testing for deep learning"
    - "machine learning testing survey landscapes and horizons"
    - "on evaluating adversarial robustness"
    - "the efficacy of shield under different threat models"
    - "formal security analysis of neural networks using symbolic intervals"
    - "a dual approach to scalable verification of deep networks"
    - "symbolic execution for deep neural networks"
    - "deepgauge multi granularity testing criteria for deep learning systems"

Connected through hyperedge HE-7 (induced by source paper "adaptive generation of unrestricted adversarial inputs"):
  with members:
    - "at gan a generative attack model for adversarial transferring on generative adversarial nets"
    - "a general framework for adversarial examples with objectives"
    - "adversarial attacks and defenses in images graphs and text a review"
    - "global adversarial attacks for assessing deep learning robustness"
    - "how good is my gan"
    - "rmdl random multimodel deep learning for classification"
    - "towards the first adversarially robust neural network model on mnist"
    - "mixtrain scalable training of formally robust neural networks"

Connected through hyperedge HE-8 (induced by source paper "correctness verification of neural networks"):
  with members:
    - "deepgauge comprehensive and multi granularity testing criteria for gauging the robustness of deep learning systems"
    - "scaling provable adversarial defenses"
    - "a dual approach to scalable verification of deep networks"
    - "deepgauge multi granularity testing criteria for deep learning systems"
    - "reachable set estimation and safety verification for piecewise linear systems with neural network controllers"
    - "certified defenses against adversarial examples"
    - "testing deep neural networks"
    - "reachable set computation and safety verification for neural networks with relu activations"

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)