human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "engineering benchmarks for planning the domains used in the deterministic part of ipc 4":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "where ignoring delete lists works local search topology in planning benchmarks")
  Member "where ignoring delete lists works local search topology in planning benchmarks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning to rank for synthesizing planning heuristics")
  Member "the deterministic part of ipc 4 an overview":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "handling pddl3 0 state trajectory constraints with temporal landmarks")
  Member "beyond hypertree width decomposition methods without decompositions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "complexity analysis of general and fractional hypertree decompositions")
  Member "ordered landmarks in planning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learn and link learning critical regions for efficient planning")
  Member "towards understanding and harnessing the potential of clause learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on cdcl based proof systems with the ordered decision strategy")
  Member "the 3rd international planning competition results and analysis":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning domain independent planning heuristics with hypergraph networks")
  Member "planning through stochastic local search and temporal action graphs in lpg":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "automated algorithm selection survey and perspectives")

Question: Please tell me which class the source paper 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)