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 symbolic approach to explaining bayesian network classifiers"
  Abstract: We propose an approach for explaining Bayesian network classifiers, which is based on compiling such classifiers into decision functions that have a tractable and symbolic form. We introduce two types of explanations for why a classifier may have classified an instance positively or negatively and suggest algorithms for computing these explanations. The first type of explanation identifies a minimal set of the currently active features that is responsible for the current classification, while the second type of explanation identifies a minimal set of features whose current state (active or not) is sufficient for the classification. We consider in particular the compilation of Naive and Latent-Tree Bayesian network classifiers into Ordered Decision Diagrams (ODDs), providing a context for evaluating our proposal using case studies and experiments based on classifiers from the literature.

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"
    - "verifying aircraft collision avoidance neural networks through linear approximations of safe regions"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "on the reasons behind decisions"):
  with members:
    - "on validating repairing and refining heuristic ml explanations"
    - "why should i trust you explaining the predictions of any classifier"
    - "the language of search"
    - "propositional independence formula variable independence and forgetting"
    - "a knowledge compilation map"

Connected through hyperedge HE-3 (induced by source paper "on validating repairing and refining heuristic ml explanations"):
  with members:
    - "methods for interpreting and understanding deep neural networks"
    - "reachability analysis of deep neural networks with provable guarantees"
    - "sanity checks for saliency maps"
    - "abduction based explanations for machine learning models"
    - "deep learning for case based reasoning through prototypes a neural network that explains its predictions"
    - "improving the adversarial robustness and interpretability of deep neural networks by regularizing their input gradients"
    - "a unified approach to interpreting model predictions"
    - "reluplex an efficient smt solver for verifying deep neural networks"

Connected through hyperedge HE-4 (induced by source paper "representation justification and explanation in a value driven agent an argumentation based approach"):
  with members:
    - "building jiminy cricket an architecture for moral agreements among stakeholders"
    - "towards a grounded dialog model for explainable artificial intelligence"
    - "explanation in artificial intelligence insights from the social sciences"
    - "prioritized norms in formal argumentation"
    - "defeasible logic programming an argumentative approach"

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)