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 "surrogate search as a way to combat harmful effects of ill behaved evaluation functions"):
  Abstract: Recently, several researchers have found that cost-based satisficing search with A* often runs into problems. Although some "work arounds" have been proposed to ameliorate the problem, there has been little concerted effort to pinpoint its origin. In this paper, we argue that the origins of this problem can be traced back to the fact that most planners that try to optimize cost also use cost-based evaluation functions (i.e., f(n) is a cost estimate). We show that cost-based evaluation functions become ill-behaved whenever there is a wide variance in action costs; something that is all too common in planning domains. The general solution to this malady is what we call a surrogatesearch, where a surrogate evaluation function that doesn't directly track the cost objective, and is resistant to cost-variance, is used. We will discuss some compelling choices for surrogate evaluation functions that are based on size rather that cost. Of particular practical interest is a cost-sensitive version of size-based evaluation function -- where the heuristic estimates the size of cheap paths, as it provides attractive quality vs. speed tradeoffs

Includes members:
  Member "the lama planner guiding cost based anytime planning with landmarks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning domain independent planning heuristics with hypergraph networks")
  Member "the ff planning system fast plan generation through heuristic search":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "what should i do now marrying reinforcement learning and symbolic planning")

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)