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: "optimization and abstraction a synergistic approach for analyzing neural network robustness"
  Abstract: In recent years, the notion of local robustness (or robustness for short) has emerged as a desirable property of deep neural networks. Intuitively, robustness means that small perturbations to an input do not cause the network to perform misclassifications. In this paper, we present a novel algorithm for verifying robustness properties of neural networks. Our method synergistically combines gradient-based optimization methods for counterexample search with abstraction-based proof search to obtain a sound and (δ -)complete decision procedure. Our method also employs a data-driven approach to learn a verification policy that guides abstract interpretation during proof search. We have implemented the proposed approach in a tool called Charon and experimentally evaluated it on hundreds of benchmarks. Our experiments show that the proposed approach significantly outperforms three state-of-the-art tools, namely AI^2, Reluplex, and Reluval.

Connected through hyperedge HE-1 (induced by source paper "metrics and methods for robustness evaluation of neural networks with generative models"):
  with members:
    - "using videos to evaluate image model robustness"
    - "seeing what a gan cannot generate"
    - "adversarial examples are a natural consequence of test error in noise"
    - "natural adversarial examples"
    - "image synthesis with a single robust classifier"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "towards photographic image manipulation with balanced growing of generative autoencoders"
    - "interpreting and evaluating neural network robustness"

Connected through hyperedge HE-2 (induced by source paper "robustness verification of support vector machines"):
  with members:
    - "formal security analysis of neural networks using symbolic intervals"
    - "adversarial framing for image and video classification"
    - "formal verification of piece wise linear feed forward neural networks"
    - "reluplex an efficient smt solver for verifying deep neural networks"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "generating natural adversarial examples"
    - "safety verification of deep neural networks"
    - "towards evaluating the robustness of neural networks"

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"
    - "fast neural network verification via shadow prices"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "proving data poisoning robustness in decision trees"):
  with members:
    - "certified defenses for data poisoning attacks"
    - "reluplex an efficient smt solver for verifying deep neural networks"
    - "targeted backdoor attacks on deep learning systems using data poisoning"
    - "curie a method for protecting svm classifier from poisoning attack"
    - "xgboost a scalable tree boosting system"
    - "towards evaluating the robustness of neural networks"
    - "intriguing properties of neural networks"
    - "poisoning attacks against support vector machines"

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.PL(Programming Languages)