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: "incremental recompilation of knowledge"
  Abstract: Approximating a general formula from above and below by Horn formulas (its Horn envelope and Horn core, respectively) was proposed by Selman and Kautz (1991, 1996) as a form of ``knowledge compilation,'' supporting rapid approximate reasoning; on the negative side, this scheme is static in that it supports no updates, and has certain complexity drawbacks pointed out by Kavvadias, Papadimitriou and Sideri (1993). On the other hand, the many frameworks and schemes proposed in the literature for theory update and revision are plagued by serious complexity-theoretic impediments, even in the Horn case, as was pointed out by Eiter and Gottlob (1992), and is further demonstrated in the present paper. More fundamentally, these schemes are not inductive, in that they may lose in a single update any positive properties of the represented sets of formulas (small size, Horn structure, etc.). In this paper we propose a new scheme, incremental recompilation, which combines Horn approximation and model-based updates; this scheme is inductive and very efficient, free of the problems facing its constituents. A set of formulas is represented by an upper and lower Horn approximation. To update, we replace the upper Horn formula by the Horn envelope of its minimum-change update, and similarly the lower one by the Horn core of its update; the key fact which enables this scheme is that Horn envelopes and cores are easy to compute when the underlying formula is the result of a minimum-change update of a Horn formula by a clause. We conjecture that efficient algorithms are possible for more complex updates.

Connected through hyperedge HE-1 (induced by source paper "efficiently enumerating hitting sets of hypergraphs arising in data profiling"):
  with members:
    - "the minimal hitting set generation problem algorithms and computation"
    - "exploiting hidden structure in selecting dimensions that distinguish vectors"
    - "randomised enumeration of small witnesses using a decision oracle"
    - "efficient enumeration of solutions produced by closure operations"
    - "a polynomial delay algorithm for enumerating minimal dominating sets in chordal graphs"
    - "paradigms for parameterized enumeration"
    - "efficient algorithms for dualizing large scale hypergraphs"
    - "new results on monotone dualization and generating hypergraph transversals"

Connected through hyperedge HE-2 (induced by source paper "achieving new upper bounds for the hypergraph duality problem through logic"):
  with members:
    - "deciding monotone duality and identifying frequent itemsets in quadratic logspace"
    - "new results on monotone dualization and generating hypergraph transversals"

Connected through hyperedge HE-3 (induced by source paper "deciding monotone duality and identifying frequent itemsets in quadratic logspace"):
  with members:
    - "new results on monotone dualization and generating hypergraph transversals"
    - "hypertree decompositions and tractable queries"

Connected through hyperedge HE-4 (induced by source paper "new results on monotone dualization and generating hypergraph transversals"):
  with members:
    - "hypertree decompositions and tractable queries"
    - "translating between horn representations and their characteristic models"

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)