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: "on a theory of probabilistic deductive databases"
  Abstract: We propose a framework for modeling uncertainty where both belief and doubt can be given independent, first-class status. We adopt probability theory as the mathematical formalism for manipulating uncertainty. An agent can express the uncertainty in her knowledge about a piece of information in the form of a confidence level, consisting of a pair of intervals of probability, one for each of her belief and doubt. The space of confidence levels naturally leads to the notion of a trilattice, similar in spirit to Fitting's bilattices. Intuitively, thep oints in such a trilattice can be ordered according to truth, information, or precision. We develop a framework for probabilistic deductive databases by associating confidence levels with the facts and rules of a classical deductive database. While the trilattice structure offers a variety of choices for defining the semantics of probabilistic deductive databases, our choice of semantics is based on the truth-ordering, which we find to be closest to the classical framework for deductive databases. In addition to proposing a declarative semantics based on valuations and an equivalent semantics based on fixpoint theory, we also propose a proof procedure and prove it sound and complete. We show that while classical Datalog query programs have a polynomial time data complexity, certain query programs in the probabilistic deductive database framework do not even terminate on some input databases. We identify a large natural class of query programs of practical interest in our framework, and show that programs in this class possess polynomial time data complexity, i.e., not only do they terminate on every input database, they are guaranteed to do so in a number of steps polynomial in the input database size.

Connected through hyperedge HE-1 (induced by source paper "probabilistic dl reasoning with pinpointing formulas a prolog based approach"):
  with members:
    - "probabilistic description logics"
    - "the pita system tabling and answer subsumption for reasoning under uncertainty"
    - "on the implementation of the probabilistic logic programming language problog"
    - "efficient description logic reasoning in prolog the dlog system"

Connected through hyperedge HE-2 (induced by source paper "evaluation of the implementation of an abstract interpretation algorithm using tabled clp"):
  with members:
    - "description implementation and evaluation of a generic design for tabled clp"
    - "tchr a framework for tabled clp"
    - "more precise yet efficient type inference for logic programs"

Connected through hyperedge HE-3 (induced by source paper "reduction of many valued into two valued modal logics"):
  with members:
    - "on paraconsistent weakening of intuitionistic negation"
    - "a new representation theorem for many valued modal logics"
    - "binary sequent calculi for truth invariance entailment of finite many valued logics"

Connected through hyperedge HE-4 (induced by source paper "deductive algorithmic knowledge"):
  with members:
    - "probabilistic algorithmic knowledge"
    - "probabilistic deduction with conditional constraints over basic events"

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.DB(Databases)