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: "verifying aircraft collision avoidance neural networks through linear approximations of safe regions"
  Abstract: The next generation of aircraft collision avoidance systems frame the problem as a Markov decision process and use dynamic programming to optimize the alerting logic. The resulting system uses a large lookup table to determine advisories given to pilots, but these tables can grow very large. To enable the system to operate on limited hardware, prior work investigated compressing the table using a deep neural network. However, ensuring that the neural network reliably issues safe advisories is important for certification. This work defines linearized regions where each advisory can be safely provided, allowing Reluplex, a neural network verification tool, to check if unsafe advisories are ever issued. A notional collision avoidance policy is generated and used to train a neural network representation. The neural networks are checked for unsafe advisories, resulting in the discovery of thousands of unsafe counterexamples.

Connected through hyperedge HE-1 (induced by source paper "robustness verification for transformers"):
  with members:
    - "correctness verification of neural networks"
    - "adversarial examples with difficult common words for paraphrase identification"
    - "certified robustness to adversarial word substitutions"
    - "art abstraction refinement guided training for provably correct neural networks"
    - "semi supervised graph classification a hierarchical graph perspective"
    - "achieving verified robustness to symbol substitutions via interval bound propagation"
    - "verification of non linear specifications for neural networks"
    - "visualbert a simple and performant baseline for vision and language"

Connected through hyperedge HE-2 (induced by source paper "automatic perturbation analysis on general computational graphs"):
  with members:
    - "robustness verification for transformers"
    - "topology attack and defense for graph neural networks an optimization perspective"
    - "fastened crown tightened neural network robustness certificates"
    - "certified robustness to adversarial word substitutions"
    - "achieving verified robustness to symbol substitutions via interval bound propagation"
    - "popqorn quantifying robustness of recurrent neural networks"
    - "asymmetric valleys beyond sharp and flat local minima"
    - "towards stable and efficient training of verifiably robust neural networks"

Connected through hyperedge HE-3 (induced by source paper "safe predictors for enforcing input output specifications"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "art abstraction refinement guided training for provably correct neural networks"
    - "end to end safe reinforcement learning through barrier functions for safety critical continuous control tasks"
    - "on the effectiveness of interval bound propagation for training verifiably robust models"
    - "towards proving the adversarial robustness of deep neural networks"
    - "optnet differentiable optimization as a layer in neural networks"
    - "reluplex an efficient smt solver for verifying deep neural networks"
    - "formal guarantees on the robustness of a classifier against adversarial manipulation"

Connected through hyperedge HE-4 (induced by source paper "robustness verification of tree based models"):
  with members:
    - "formal verification of input output mappings of tree ensembles"
    - "formal verification of decision tree ensemble model and detection of its violating input value ranges"
    - "robust decision trees against adversarial examples"
    - "a convex relaxation barrier to tight robustness verification of neural networks"
    - "verifying robustness of gradient boosted models"
    - "scaling provable adversarial defenses"
    - "adversarial risk and the dangers of evaluating against weak attacks"
    - "mixtrain scalable training of formally robust 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.SY(Systems and Control)