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: "fast neural network verification via shadow prices"
  Abstract: To use neural networks in safety-critical settings it is paramount to provide assurances on their runtime operation. Recent work on ReLU networks has sought to verify whether inputs belonging to a bounded box can ever yield some undesirable output. Input-splitting procedures, a particular type of verification mechanism, do so by recursively partitioning the input set into smaller sets. The efficiency of these methods is largely determined by the number of splits the box must undergo before the property can be verified. In this work, we propose a new technique based on shadow prices that fully exploits the information of the problem yielding a more efficient generation of splits than the state-of-the-art. Results on the Airborne Collision Avoidance System (ACAS) benchmark verification tasks show a considerable reduction in the partitions generated which substantially reduces computation times. These results open the door to improved verification methods for a wide variety of machine learning applications including vision and control.

Connected through hyperedge HE-1 (induced by source paper "verification of neural network control policy under persistent adversarial perturbation"):
  with members:
    - "efficient and accurate estimation of lipschitz constants for deep neural networks"
    - "the lyapunov neural network adaptive stability certification for safe learning of dynamical systems"
    - "stability certified reinforcement learning a control theoretic perspective"
    - "verisig verifying safety properties of hybrid systems with neural network controllers"
    - "a lyapunov based approach to safe reinforcement learning"
    - "reachability analysis and safety verification for neural network control systems"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "adversarial attacks on neural network policies"

Connected through hyperedge HE-2 (induced by source paper "branch and bound for piecewise linear neural network verification"):
  with members:
    - "neural network branching for neural network verification"
    - "formal security analysis of neural networks using symbolic intervals"
    - "a dual approach to scalable verification of deep networks"
    - "efficient formal safety analysis of neural networks"
    - "an approach to reachability analysis for feed forward relu neural networks"
    - "output reachable set estimation and verification for multi layer neural networks"
    - "formal guarantees on the robustness of a classifier against adversarial manipulation"
    - "measuring neural net robustness with constraints"

Connected through hyperedge HE-3 (induced by source paper "neural network branching for neural network verification"):
  with members:
    - "exact combinatorial optimization with graph convolutional neural networks"
    - "optimization and abstraction a synergistic approach for analyzing neural network robustness"
    - "branch and bound for piecewise linear neural network verification"
    - "efficient formal safety analysis of neural networks"
    - "evaluating robustness of neural networks with mixed integer programming"
    - "learning combinatorial optimization algorithms over graphs"
    - "reluplex an efficient smt solver for verifying deep neural networks"
    - "neural combinatorial optimization with reinforcement learning"

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)