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: "structure and problem hardness goal asymmetry and dpll proofs in sat based planning"
  Abstract: In Verification and in (optimal) AI Planning, a successful method is to#N#formulate the application as boolean satisfiability (SAT), and solve it with#N#state-of-the-art DPLL-based procedures. There is a lack of understanding of why#N#this works so well. Focussing on the Planning context, we identify a form of#N#problem structure concerned with the symmetrical or asymmetrical nature of the#N#cost of achieving the individual planning goals. We quantify this sort of#N#structure with a simple numeric parameter called AsymRatio, ranging between 0#N#and 1. We run experiments in 10 benchmark domains from the International#N#Planning Competitions since 2000; we show that AsymRatio is a good indicator of#N#SAT solver performance in 8 of these domains. We then examine carefully crafted#N#synthetic planning domains that allow control of the amount of structure, and#N#that are clean enough for a rigorous analysis of the combinatorial search#N#space. The domains are parameterized by size, and by the amount of structure.#N#The CNFs we examine are unsatisfiable, encoding one planning step less than the#N#length of the optimal plan. We prove upper and lower bounds on the size of the#N#best possible DPLL refutations, under different settings of the amount of#N#structure, as a function of size. We also identify the best possible sets of#N#branching variables (backdoors). With minimum AsymRatio, we prove exponential#N#lower bounds, and identify minimal backdoors of size linear in the number of#N#variables. With maximum AsymRatio, we identify logarithmic DPLL refutations#N#(and backdoors), showing a doubly exponential gap between the two structural#N#extreme cases. The reasons for this behavior -- the proof arguments --#N#illuminate the prototypical patterns of structure causing the empirical#N#behavior observed in the competition benchmarks.

Connected through hyperedge HE-1 (induced by source paper "sas planning as satisfiability"):
  with members:
    - "the fast downward planning system"
    - "the ff planning system fast plan generation through heuristic search"

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)