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: "a logical approach to efficient max sat solving"
  Abstract: Weighted Max-SAT is the optimization version of SAT and many important problems can be naturally encoded as such. Solving weighted Max-SAT is an important problem from both a theoretical and a practical point of view. In recent years, there has been considerable interest in finding efficient solving techniques. Most of this work focus on the computation of good quality lower bounds to be used within a branch and bound DPLL-like algorithm. Most often, these lower bounds are described in a procedural way. Because of that, it is difficult to realize the {\em logic} that is behind. #R##N#In this paper we introduce an original framework for Max-SAT that stresses the parallelism with classical SAT. Then, we extend the two basic SAT solving techniques: {\em search} and {\em inference}. We show that many algorithmic {\em tricks} used in state-of-the-art Max-SAT solvers are easily expressable in {\em logic} terms with our framework in a unified manner. #R##N#Besides, we introduce an original search algorithm that performs a restricted amount of {\em weighted resolution} at each visited node. We empirically compare our algorithm with a variety of solving alternatives on several benchmarks. Our experiments, which constitute to the best of our knowledge the most comprehensive Max-sat evaluation ever reported, show that our algorithm is generally orders of magnitude faster than any competitor.

Connected through hyperedge HE-1 (induced by source paper "towards a better understanding of partial weighted maxsat proof systems"):
  with members:
    - "circular yet sound proofs"
    - "on tackling the limits of resolution in sat solving"

Connected through hyperedge HE-2 (induced by source paper "on tackling the limits of resolution in sat solving"):
  with members:
    - "incremental cardinality constraints for maxsat"
    - "on using unsatisfiability for solving maximum satisfiability"

Connected through hyperedge HE-3 (induced by source paper "fuzzy maximum satisfiability"):
  with members:
    - "solving linux upgradeability problems using boolean optimization"
    - "cause clue clauses error localization using maximum satisfiability"
    - "new inference rules for max sat"
    - "on using unsatisfiability for solving maximum satisfiability"

Connected through hyperedge HE-4 (induced by source paper "hinge loss markov random fields and probabilistic soft logic"):
  with members:
    - "reinforcement and imitation learning via interactive no regret learning"
    - "exploiting social network structure for person to person sentiment analysis"
    - "inferring user preferences by probabilistic logical reasoning over social networks"
    - "tighter linear program relaxations for high order graphical models"
    - "hinge loss markov random fields convex inference for structured prediction"
    - "a hypergraph partitioned vertex programming approach for large scale consensus optimization"
    - "object oriented bayesian networks"
    - "spook a system for probabilistic object oriented knowledge representation"

Connected through hyperedge HE-5 (induced by source paper "algorithms for weighted boolean optimization"):
  with members:
    - "new inference rules for max sat"
    - "on using unsatisfiability for solving maximum satisfiability"

Connected through hyperedge HE-6 (induced by source paper "new inference rules for max sat"):
  with members:
    - "a simple model to generate hard satisfiable instances"
    - "arc consistency for soft constraints"

Connected through hyperedge HE-7 (induced by source paper "on using unsatisfiability for solving maximum satisfiability"):
  with members:
    - "new inference rules for max sat"

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)