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 unified view of piecewise linear neural network verification"
  Abstract: The success of Deep Learning and its potential use in many safety-critical applications has motivated research on formal verification of Neural Network (NN) models. Despite the reputation of learned NN models to behave as black boxes and the theoretical hardness of proving their properties, researchers have been successful in verifying some classes of models by exploiting their piecewise linear structure and taking insights from formal methods such as Satisifiability Modulo Theory. These methods are however still far from scaling to realistic neural networks. To facilitate progress on this crucial area, we make two key contributions. First, we present a unified framework that encompasses previous methods. This analysis results in the identification of new methods that combine the strengths of multiple existing approaches, accomplishing a speedup of two orders of magnitude compared to the previous state of the art. Second, we propose a new data set of benchmarks which includes a collection of previously released testcases. We use the benchmark to provide the first experimental comparison of existing algorithms and identify the factors impacting the hardness of verification problems.

Connected through hyperedge HE-1 (induced by source paper "reward potentials for planning with learned neural network transition models"):
  with members:
    - "compact and efficient encodings for planning in factored state and action spaces with learned binarized neural network transition models"
    - "evaluating robustness of neural networks with mixed integer programming"
    - "provable defenses against adversarial examples via the convex outer adversarial polytope"
    - "formal verification of piece wise linear feed forward neural networks"
    - "reluplex an efficient smt solver for verifying deep neural networks"
    - "safety verification of deep neural networks"
    - "natural language processing almost from scratch"

Connected through hyperedge HE-2 (induced by source paper "formal verification of neural network controlled autonomous systems"):
  with members:
    - "adversarial sample detection for deep neural network through model mutation testing"
    - "deepgauge comprehensive and multi granularity testing criteria for gauging the robustness of deep learning systems"
    - "automated verification of neural networks advances challenges and perspectives"
    - "robust deep reinforcement learning for security and safety in autonomous vehicle systems"
    - "agi safety literature review"
    - "deepgauge multi granularity testing criteria for deep learning systems"
    - "deepmutation mutation testing of deep learning systems"
    - "concolic testing for deep neural networks"

Connected through hyperedge HE-3 (induced by source paper "reachability analysis of deep neural networks with provable guarantees"):
  with members:
    - "concolic testing for deep neural networks"
    - "global robustness evaluation of deep neural networks with provable guarantees for l0 norm"
    - "provable defenses against adversarial examples via the convex outer adversarial polytope"
    - "an approach to reachability analysis for feed forward relu neural networks"
    - "maximum resilience of artificial neural networks"
    - "output range analysis for deep neural networks"
    - "formal verification of piece wise linear feed forward neural networks"
    - "feature guided black box safety testing of 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.AI(Artificial Intelligence)