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: "using answer set programming for pattern mining"
  Abstract: Serial pattern mining consists in extracting the frequent sequential patterns from a unique sequence of itemsets. This paper explores the ability of a declarative language, such as Answer Set Programming (ASP), to solve this issue efficiently. We propose several ASP implementations of the frequent sequential pattern mining task: a non-incremental and an incremental resolution. The results show that the incremental resolution is more efficient than the non-incremental one, but both ASP programs are less efficient than dedicated algorithms. Nonetheless, this approach can be seen as a first step toward a generic framework for sequential pattern mining with constraints.

Connected through hyperedge HE-1 (induced by source paper "hybrid asp based approach to pattern mining"):
  with members:
    - "reductions for frequency based data mining problems"
    - "knowledge representation analysis of graph mining"
    - "an efficient algorithm for mining frequent sequence with constraint programming"
    - "graph isomorphism in quasipolynomial time"
    - "constraint based sequence mining using constraint programming"
    - "a constraint programming approach for mining sequential patterns in a sequence database"
    - "design and implementation of aggregate functions in the dlv system"

Connected through hyperedge HE-2 (induced by source paper "efficiency analysis of asp encodings for sequential pattern mining tasks"):
  with members:
    - "a sat model to mine flexible sequences in transactional datasets"
    - "constraint based sequence mining using constraint programming"
    - "predicate logic as a modeling language modeling and solving some machine learning and data mining problems with idp3"
    - "clingo asp control preliminary report"
    - "a constraint programming approach for mining sequential patterns in a sequence database"
    - "the dlv system for knowledge representation and reasoning"

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)