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: "minimization for generalized boolean formulas"
  Abstract: The minimization problem for propositional formulas is an important optimization problem in the second level of the polynomial hierarchy. In general, the problem is Sigma-2-complete under Turing reductions, but restricted versions are tractable. We study the complexity of minimization for formulas in two established frameworks for restricted propositional logic: The Post framework allowing arbitrarily nested formulas over a set of Boolean connectors, and the constraint setting, allowing generalizations of CNF formulas. In the Post case, we obtain a dichotomy result: Minimization is solvable in polynomial time or coNP-hard. This result also applies to Boolean circuits. For CNF formulas, we obtain new minimization algorithms for a large class of formulas, and give strong evidence that we have covered all polynomial-time cases.

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"
    - "redundancy deduction schemes and minimum size bases for association rules"
    - "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 "logical reduction of metarules"):
  with members:
    - "inductive general game playing"
    - "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"
    - "redundancy in logic i cnf propositional formulae"
    - "redundancy in logic ii 2cnf and horn propositional formulae"

Connected through hyperedge HE-3 (induced by source paper "generalising unit refutation completeness and slur via nested input resolution"):
  with members:
    - "pebble games proof complexity and time space trade offs"
    - "towards a theory of good sat representations"
    - "circuit complexity and decompositions of global constraints"
    - "a knowledge compilation map"

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.CC(Computational Complexity)