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 provable defense for deep residual networks"
  Abstract: We present a training system, which can provably defend significantly larger neural networks than previously possible, including ResNet-34 and DenseNet-100. Our approach is based on differentiable abstract interpretation and introduces two novel concepts: (i) abstract layers for fine-tuning the precision and scalability of the abstraction, (ii) a flexible domain specific language (DSL) for describing training objectives that combine abstract and concrete losses with arbitrary specifications. Our training method is implemented in the DiffAI system.

Connected through hyperedge HE-1 (induced by source paper "parot a practical framework for robust deep neural network training"):
  with members:
    - "algorithms for verifying deep neural 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 "towards stable and efficient training of verifiably robust neural networks"):
  with members:
    - "meshadv adversarial meshes for visual recognition"
    - "certified adversarial robustness via randomized smoothing"
    - "theoretically principled trade off between robustness and accuracy"
    - "provably robust deep learning via adversarially trained smoothed classifiers"
    - "verification of non linear specifications for neural networks"
    - "a convex relaxation barrier to tight robustness verification of neural networks"
    - "scaling provable adversarial defenses"
    - "spatially transformed adversarial examples"

Connected through hyperedge HE-3 (induced by source paper "universal approximation with certified networks"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "universal lipschitz approximation in bounded depth neural networks"
    - "a convex relaxation barrier to tight robustness verification of neural networks"
    - "scaling provable adversarial defenses"
    - "on the geometry of adversarial examples"
    - "on the effectiveness of interval bound propagation for training verifiably robust models"
    - "sorting out lipschitz function approximation"
    - "a dual approach to scalable verification of deep networks"

Connected through hyperedge HE-4 (induced by source paper "perfectly parallel fairness certification of neural networks"):
  with members:
    - "probabilistic verification of fairness properties via concentration"
    - "a survey on bias and fairness in machine learning"
    - "analyzing deep neural networks with symbolic propagation towards higher precision and faster verification"
    - "the efficacy of shield under different threat models"
    - "deepsearch simple and effective blackbox fuzzing of deep neural networks"
    - "formal security analysis of neural networks using symbolic intervals"
    - "symbolic execution for deep neural networks"
    - "concolic testing for deep neural networks"

Connected through hyperedge HE-5 (induced by source paper "online robustness training for deep reinforcement learning"):
  with members:
    - "certified adversarial robustness for deep reinforcement learning"
    - "scaling provable adversarial defenses"
    - "adversary a3c for robust reinforcement learning"
    - "robust deep reinforcement learning for security and safety in autonomous vehicle systems"
    - "training verified learners with learned verifiers"
    - "online robust policy learning in the presence of unknown adversaries"
    - "certified defenses against adversarial examples"
    - "rainbow combining improvements in deep reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "fast and stable interval bounds propagation for training verifiably robust models"):
  with members:
    - "a convex relaxation barrier to tight robustness verification of neural networks"
    - "on certifying non uniform bound against adversarial attacks"
    - "on the effectiveness of interval bound propagation for training verifiably robust models"
    - "a dual approach to scalable verification of deep networks"
    - "training verified learners with learned verifiers"
    - "training for faster adversarial robustness verification via inducing relu stability"
    - "generating adversarial examples with adversarial networks"
    - "efficient neural network robustness certification with general activation functions"

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)