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: "propositional abduction with implicit hitting sets"
  Abstract: Logic-based abduction finds important applications in artificial intelligence and related areas. One application example is in finding explanations for observed phenomena. Propositional abduction is a restriction of abduction to the propositional domain, and complexity-wise is in the second level of the polynomial hierarchy. Recent work has shown that exploiting implicit hitting sets and propositional satisfiability (SAT) solvers provides an efficient approach for propositional abduction. This paper investigates this earlier work and proposes a number of algorithmic improvements. These improvements are shown to yield exponential reductions in the number of SAT solver calls. More importantly, the experimental results show significant performance improvements compared to the the best approaches for propositional abduction.

Connected through hyperedge HE-1 (induced by source paper "on validating repairing and refining heuristic ml explanations"):
  with members:
    - "a symbolic approach to explaining bayesian network classifiers"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "abduction based explanations for machine learning models"):
  with members:
    - "interpreting cnns via decision trees"
    - "the intriguing properties of model explanations"
    - "methods for interpreting and understanding deep neural networks"
    - "reachability analysis of deep neural networks with provable guarantees"
    - "learning explanatory rules from noisy data"
    - "deep learning for case based reasoning through prototypes a neural network that explains its predictions"
    - "human level intelligence or animal like abilities"
    - "feature guided black box safety testing of deep neural networks"

Connected through hyperedge HE-3 (induced by source paper "model based diagnosis of multiple observations with implicit hitting sets"):
  with members:
    - "algorithms for implicit hitting set problems"
    - "detecting inconsistencies in large biological networks with answer set programming"
    - "cause clue clauses error localization using maximum satisfiability"
    - "answer sets for consistent query answering in inconsistent databases"
    - "model based diagnosis using structured system descriptions"

Connected through hyperedge HE-4 (induced by source paper "horn maximum satisfiability reductions algorithms applications"):
  with members:
    - "efficient mus enumeration of horn formulae with applications to axiom pinpointing"

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)