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: "soft goals can be compiled away"
  Abstract: Soft goals extend the classical model of planning with a simple model of preferences. The best plans are then not the ones with least cost but the ones with maximum utility, where the utility of a plan is the sum of the utilities of the soft goals achieved minus the plan cost. Finding plans with high utility appears to involve two linked problems: choosing a subset of soft goals to achieve and finding a low-cost plan to achieve them. New search algorithms and heuristics have been developed for planning with soft goals, and a new track has been introduced in the International Planning Competition (IPC) to test their performance. In this note, we show however that these extensions are not needed: soft goals do not increase the expressive power of the basic model of planning with action costs, as they can easily be compiled away. We apply this compilation to the problems of the net-benefit track of the most recent IPC, and show that optimal and satisficing cost-based planners do better on the compiled problems than optimal and satisficing netbenefit planners on the original problems with explicit soft goals. Furthermore, we show that penalties, or negative preferences expressing conditions to avoid, can also be compiled away using a similar idea.

Connected through hyperedge HE-1 (induced by source paper "economics of human ai ecosystem value bias and lost utility in multi dimensional gaps"):
  with members:
    - "explaining explanations in ai"
    - "towards providing explanations for ai planner decisions"
    - "explainable security"
    - "explanation in artificial intelligence insights from the social sciences"
    - "what does explainable ai really mean a new conceptualization of perspectives"
    - "flexibly bounded rationality and marginalization of irrationality theories for decision making"
    - "ordered landmarks in planning"
    - "decision theoretic planning structural assumptions and computational leverage"

Connected through hyperedge HE-2 (induced by source paper "automated tactical decision planning model with strategic values guidance for local action value ambiguity"):
  with members:
    - "economics of human ai ecosystem value bias and lost utility in multi dimensional gaps"

Connected through hyperedge HE-3 (induced by source paper "knowledge engineering for planning based hypothesis generation"):
  with members:
    - "the lama planner guiding cost based anytime planning with landmarks"

Connected through hyperedge HE-4 (induced by source paper "online speedup learning for optimal planning"):
  with members:
    - "implicit abstraction heuristics"
    - "compiling uncertainty away in conformant planning problems with bounded width"
    - "marvin a heuristic search planner with online macro action learning"
    - "the fast downward planning system"
    - "macro ff improving ai planning with automatically learned macro operators"
    - "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)