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: "deep neural network compression for aircraft collision avoidance systems"
  Abstract: One approach to designing decision-making logic for an aircraft collision avoidance system frames the problem as a Markov decision process and optimizes the system using dynamic programming. The re...

Connected through hyperedge HE-1 (induced by source paper "reludiff differential verification of deep neural networks"):
  with members:
    - "towards compact and robust deep neural networks"
    - "formal security analysis of neural networks using symbolic intervals"
    - "a dual approach to scalable verification of deep networks"
    - "deepgauge multi granularity testing criteria for deep learning systems"
    - "concolic testing for deep neural networks"
    - "reachability analysis of deep neural networks with provable guarantees"
    - "efficient formal safety analysis of neural networks"
    - "certified defenses against adversarial examples"

Connected through hyperedge HE-2 (induced by source paper "dtcontrol decision tree learning algorithms for controller representation"):
  with members:
    - "compositional synthesis of symbolic models for networks of switched systems"
    - "strategy representation by decision trees with linear classifiers"
    - "strategy representation by decision trees in reactive synthesis"
    - "optimal symbolic controllers determinization for bdd storage"
    - "software fault tolerance for cyber physical systems via full system restart"
    - "invariance feedback entropy of uncertain control systems"
    - "quantitative implementation strategies for safety controllers"
    - "counterexample explanation by learning small strategies in markov decision processes"

Connected through hyperedge HE-3 (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"
    - "how important is a neuron"
    - "formal security analysis of neural networks using symbolic intervals"
    - "reachable set estimation and safety verification for piecewise linear systems with neural network controllers"
    - "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-4 (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"
    - "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"
    - "reachable set estimation and safety verification for piecewise linear systems with neural network controllers"
    - "efficient neural network robustness certification with general activation functions"
    - "certified defenses against adversarial examples"

Connected through hyperedge HE-5 (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"
    - "formal security analysis of neural networks using symbolic intervals"
    - "reachable set estimation and safety verification for piecewise linear systems with neural network controllers"
    - "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-6 (induced by source paper "using quantifier elimination to enhance the safety assurance of deep neural networks"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "algorithms for verifying deep neural networks"
    - "formal security analysis of neural networks using symbolic intervals"
    - "safety and trustworthiness of deep neural networks a survey"
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "towards proving the adversarial robustness of deep neural networks"
    - "deepsafe a data driven approach for checking adversarial robustness in neural networks"
    - "reluplex an efficient smt solver for verifying 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.LG(Machine Learning)