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: "redundancy deduction schemes and minimum size bases for association rules"
  Abstract: Association rules are among the most widely employed data analysis methods in the field of Data Mining. An association rule is a form of partial implication between two sets of binary variables. In the most common approach, association rules are parameterized by a lower bound on their confidence, which is the empirical conditional probability of their consequent given the antecedent, and/or by some other parameter bounds such as "support" or deviation from independence. We study here notions of redundancy among association rules from a fundamental perspective. We see each transaction in a dataset as an interpretation (or model) in the propositional logic sense, and consider existing notions of redundancy, that is, of logical entailment, among association rules, of the form "any dataset in which this first rule holds must obey also that second rule, therefore the second is redundant". We discuss several existing alternative definitions of redundancy between association rules and provide new characterizations and relationships among them. We show that the main alternatives we discuss correspond actually to just two variants, which differ in the treatment of full-confidence implications. For each of these two notions of redundancy, we provide a sound and complete deduction calculus, and we show how to construct complete bases (that is, axiomatizations) of absolutely minimum size in terms of the number of rules. We explore finally an approach to redundancy with respect to several association rules, and fully characterize its simplest case of two partial premises.

Connected through hyperedge HE-1 (induced by source paper "sld resolution reduction of second order horn fragments technical report"):
  with members:
    - "logical rule induction and theory learning using neural theorem proving"
    - "exploiting answer set programming with external sources for meta interpretive learning"
    - "learning explanatory rules from noisy data"
    - "end to end differentiable proving"
    - "deep learning for ontology reasoning"
    - "minimization for generalized boolean formulas"
    - "redundancy deduction schemes and minimum size bases for association rules"
    - "redundancy in logic i cnf propositional formulae"

Connected through hyperedge HE-2 (induced by source paper "relative entailment among probabilistic implications"):
  with members:
    - "entailment among probabilistic implications"
    - "redundancy deduction schemes and minimum size bases for association rules"

Connected through hyperedge HE-3 (induced by source paper "quantitative redundancy in partial implications"):
  with members:
    - "entailment among probabilistic implications"
    - "formal and computational properties of the confidence boost of association rules"
    - "redundancy deduction schemes and minimum size bases for association rules"

Connected through hyperedge HE-4 (induced by source paper "entailment among probabilistic implications"):
  with members:
    - "transforming outermost into context sensitive rewriting"
    - "redundancy deduction schemes and minimum size bases for association rules"

Connected through hyperedge HE-5 (induced by source paper "the joy of implications aka pure horn functions mainly a survey"):
  with members:
    - "hardness results for approximate pure horn cnf formulae minimization"
    - "efficient algorithms for dualizing large scale hypergraphs"
    - "redundancy deduction schemes and minimum size bases for association rules"

Connected through hyperedge HE-6 (induced by source paper "formal and computational properties of the confidence boost of association rules"):
  with members:
    - "closed set based discovery of bases of association rules"
    - "border algorithms for computing hasse diagrams of arbitrary lattices"
    - "redundancy deduction schemes and minimum size bases for association rules"

Connected through hyperedge HE-7 (induced by source paper "selecting attributes for sport forecasting using formal concept analysis"):
  with members:
    - "redundancy deduction schemes and minimum size bases for association rules"

Connected through hyperedge HE-8 (induced by source paper "border algorithms for computing hasse diagrams of arbitrary lattices"):
  with members:
    - "redundancy deduction schemes and minimum size bases for association rules"
    - "orthomodular lattices foulis semigroups and dagger kernel categories"

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.LO(Logic in Computer Science)