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: "constraint based sequence mining using constraint programming"
  Abstract: The goal of constraint-based sequence mining is to find sequences of symbols that are included in a large number of input sequences and that satisfy some constraints specified by the user. Many constraints have been proposed in the literature, but a general framework is still missing. We investigate the use of constraint programming as general framework for this task. We first identify four categories of constraints that are applicable to sequence mining. We then propose two constraint programming formulations. The first formulation introduces a new global constraint called exists-embedding. This formulation is the most efficient but does not support one type of constraint. To support such constraints, we develop a second formulation that is more general but incurs more overhead. Both formulations can use the projected database technique used in specialised algorithms. Experiments demonstrate the flexibility towards constraint-based settings and compare the approach to existing methods.

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"
    - "using answer set programming for pattern mining"
    - "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 "negpspan efficient extraction of negative sequential patterns with embedding constraints"):
  with members:
    - "declarative sequential pattern mining of care pathways"
    - "discriminant chronicles mining application to care pathways analytics"
    - "anytime discovery of a diverse set of patterns with monte carlo tree search"

Connected through hyperedge HE-3 (induced by source paper "constraint based sequential pattern mining with decision diagrams"):
  with members:
    - "a global constraint for mining sequential patterns with gap constraint"

Connected through hyperedge HE-4 (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"
    - "predicate logic as a modeling language modeling and solving some machine learning and data mining problems with idp3"
    - "using answer set programming for pattern mining"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "an efficient algorithm for mining frequent sequence with constraint programming"):
  with members:
    - "a global constraint for mining sequential patterns with gap constraint"
    - "prefix projection global constraint for sequential pattern mining"

Connected through hyperedge HE-6 (induced by source paper "a global constraint for mining sequential patterns with gap constraint"):
  with members:
    - "prefix projection global constraint for sequential pattern mining"
    - "a constraint programming approach for mining sequential patterns in a sequence database"

Connected through hyperedge HE-7 (induced by source paper "prefix projection global constraint for sequential pattern mining"):
  with members:
    - "a constraint programming approach for mining sequential patterns in a sequence database"

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)