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: "reachable set estimation and safety verification for piecewise linear systems with neural network controllers"
  Abstract: In this work, the reachable set estimation and safety verification problems for a class of piecewise linear systems equipped with neural network controllers are addressed. The neural network is considered to consist of Rectified Linear Unit (ReLU) activation functions. A layer-by-layer approach is developed for the output reachable set computation of ReLU neural networks. The computation is formulated in the form of a set of manipulations for a union of polytopes. Based on the output reachable set for neural network controllers, the output reachable set for a piecewise linear feedback control system can be estimated iteratively for a given finite-time interval. With the estimated output reachable set, the safety verification for piecewise linear systems with neural network controllers can be performed by checking the existence of intersections of unsafe regions and output reach set. A numerical example is presented to illustrate the effectiveness of our approach.

Connected through hyperedge HE-1 (induced by source paper "correctness verification of neural networks"):
  with members:
    - "algorithms for verifying deep neural networks"
    - "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"
    - "certified defenses against adversarial examples"
    - "testing deep neural networks"
    - "reachable set computation and safety verification for neural networks with relu activations"

Connected through hyperedge HE-2 (induced by source paper "computing linear restrictions of neural networks"):
  with members:
    - "provable certificates for adversarial examples fitting a ball in the union of polytopes"
    - "deep neural network compression for aircraft collision avoidance systems"
    - "how important is a neuron"
    - "formal security analysis of neural networks using symbolic intervals"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "reachable set computation and safety verification for neural networks with relu activations"
    - "network dissection quantifying interpretability of deep visual representations"
    - "formal verification of piece wise linear feed forward neural networks"

Connected through hyperedge HE-3 (induced by source paper "algorithms for verifying deep neural networks"):
  with members:
    - "analyzing deep neural networks with symbolic propagation towards higher precision and faster verification"
    - "juliareach a toolbox for set based reachability"
    - "deep neural network compression for aircraft collision avoidance systems"
    - "automated verification of neural networks advances challenges and perspectives"
    - "formal security analysis of neural networks using symbolic intervals"
    - "a dual approach to scalable verification of deep networks"
    - "efficient neural network robustness certification with general activation functions"
    - "certified defenses against adversarial examples"

Connected through hyperedge HE-4 (induced by source paper "a symbolic neural network representation and its application to understanding verifying and patching networks"):
  with members:
    - "an inductive synthesis framework for verifiable reinforcement learning"
    - "deep neural network compression for aircraft collision avoidance systems"
    - "formal security analysis of neural networks using symbolic intervals"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "audio adversarial examples targeted attacks on speech to text"
    - "verifiable reinforcement learning via policy extraction"
    - "reachable set computation and safety verification for neural networks with relu activations"
    - "formal verification of piece wise linear feed forward neural networks"

Connected through hyperedge HE-5 (induced by source paper "verification for machine learning autonomy and neural networks survey"):
  with members:
    - "pragmatic pedagogic value alignment"
    - "deepgauge comprehensive and multi granularity testing criteria for gauging the robustness of deep learning systems"
    - "deep reinforcement learning for autonomous driving"
    - "a dual approach to scalable verification of deep networks"
    - "neural guided deductive search for real time program synthesis from examples"
    - "reachable set estimation and verification for neural network models of nonlinear dynamic systems"
    - "testing deep neural networks"
    - "provable defenses against adversarial examples via the convex outer adversarial polytope"

Connected through hyperedge HE-6 (induced by source paper "reachability analysis and safety verification for neural network control systems"):
  with members:
    - "an approach to reachability analysis for feed forward relu neural networks"
    - "output range analysis for deep neural networks"
    - "reluplex an efficient smt solver for verifying deep neural networks"
    - "end to end learning for self driving cars"

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.SY(Systems and Control)