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: "new inference rules for max sat"
  Abstract: Exact Max-SAT solvers, compared with SAT solvers, apply little inference at each node of the proof tree. Commonly used SAT inference rules like unit propagation produce a simplified formula that preserves satisfiability but, unfortunately, solving the Max-SAT problem for the simplified formula is not equivalent to solving it for the original formula. In this paper, we define a number of original inference rules that, besides being applied efficiently, transform Max-SAT instances into equivalent Max-SAT instances which are easier to solve. The soundness of the rules, that can be seen as refinements of unit resolution adapted to Max-SAT, are proved in a novel and simple way via an integer programming transformation. With the aim of finding out how powerful the inference rules are in practice, we have developed a new Max-SAT solver, called MaxSatz, which incorporates those rules, and performed an experimental investigation. The results provide empirical evidence that MaxSatz is very competitive, at least, on random Max-2SAT, random Max-3SAT, Max-Cut, and Graph 3-coloring instances, as well as on the benchmarks from the Max-SAT Evaluation 2006.

Connected through hyperedge HE-1 (induced by source paper "learning action models from disordered and noisy plan traces"):
  with members:
    - "learning portable representations for high level planning"
    - "towards stable symbol grounding with zero suppressed state autoencoder"
    - "learning strips action models with classical planning"
    - "extracting action sequences from texts based on deep reinforcement learning"
    - "efficient safe and probably approximately complete learning of action models"
    - "classical planning in deep latent space bridging the subsymbolic symbolic boundary"
    - "distributed representations of sentences and documents"
    - "learning strips operators from noisy and incomplete observations"

Connected through hyperedge HE-2 (induced by source paper "fuzzy maximum satisfiability"):
  with members:
    - "solving linux upgradeability problems using boolean optimization"
    - "cause clue clauses error localization using maximum satisfiability"
    - "on using unsatisfiability for solving maximum satisfiability"
    - "a logical approach to efficient max sat solving"

Connected through hyperedge HE-3 (induced by source paper "numvc an efficient local search algorithm for minimum vertex cover"):
  with members:
    - "dynamic local search for the maximum clique problem"
    - "a simple model to generate hard satisfiable instances"
    - "a novel evolutionary formulation of the maximum independent set problem"
    - "optimal schedules for parallelizing anytime algorithms the case of shared resources"
    - "exact phase transitions in random constraint satisfaction problems"

Connected through hyperedge HE-4 (induced by source paper "algorithms for weighted boolean optimization"):
  with members:
    - "on using unsatisfiability for solving maximum satisfiability"
    - "a logical approach to efficient max sat solving"

Connected through hyperedge HE-5 (induced by source paper "on using unsatisfiability for solving maximum satisfiability"):
  with members:
    - "a logical approach to efficient max sat solving"

Connected through hyperedge HE-6 (induced by source paper "a logical approach to efficient max sat solving"):
  with members:
    - "on the practical use of variable elimination in constraint optimization problems still life as a case study"
    - "arc consistency for soft constraints"

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)