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: "flucap a heuristic search planner for first order mdps"
  Abstract: We present a heuristic search algorithm for solving first-order Markov Decision Processes (FOMDPs). Our approach combines first-order state abstraction that avoids evaluating states individually, and heuristic search that avoids evaluating all states. Firstly, in contrast to existing systems, which start with propositionalizing the FOMDP and then perform state abstraction on its propositionalized version we apply state abstraction directly on the FOMDP avoiding propositionalization. This kind of abstraction is referred to as first-order state abstraction. Secondly, guided by an admissible heuristic, the search is restricted to those states that are reachable from the initial state. We demonstrate the usefulness of the above techniques for solving FOMDPs with a system, referred to as FluCaP (formerly, FCPlanner), that entered the probabilistic track of the 2004 International Planning Competition (IPC'2004) and demonstrated an advantage over other planners on the problems represented in first-order terms.

Connected through hyperedge HE-1 (induced by source paper "the complexity of reasoning with fodd and gfodd"):
  with members:
    - "solving relational mdps with exogenous events and additive rewards"
    - "spudd stochastic planning using decision diagrams"
    - "first order mixed integer linear programming"
    - "probabilistic relational planning with first order decision diagrams"
    - "first order decision diagrams for relational mdps"

Connected through hyperedge HE-2 (induced by source paper "solving relational mdps with exogenous events and additive rewards"):
  with members:
    - "spudd stochastic planning using decision diagrams"
    - "probabilistic relational planning with first order decision diagrams"
    - "first order decision diagrams for relational mdps"
    - "efficient solution algorithms for factored mdps"
    - "decision theoretic planning structural assumptions and computational leverage"

Connected through hyperedge HE-3 (induced by source paper "probabilistic event calculus for event recognition"):
  with members:
    - "a probabilistic logic programming event calculus"
    - "location based reasoning about complex multi agent behavior"
    - "probabilistic reasoning about actions in nonmonotonic causal theories"
    - "improving the accuracy and efficiency of map inference for markov logic"
    - "sampling first order logical particles"
    - "counting belief propagation"
    - "on the implementation of the probabilistic logic programming language problog"
    - "a logic programming approach to activity recognition"

Connected through hyperedge HE-4 (induced by source paper "generating optimal plans in highly dynamic domains"):
  with members:
    - "a domain independent algorithm for plan adaptation"

Connected through hyperedge HE-5 (induced by source paper "probabilistic relational planning with first order decision diagrams"):
  with members:
    - "spudd stochastic planning using decision diagrams"
    - "exploiting first order regression in inductive policy selection"
    - "policy iteration for relational mdps"
    - "first order decision diagrams for relational mdps"
    - "approximate policy iteration with a policy language bias solving relational markov decision processes"
    - "efficient solution algorithms for factored mdps"
    - "decision theoretic planning structural assumptions and computational leverage"

Connected through hyperedge HE-6 (induced by source paper "automatic induction of bellman error features for probabilistic planning"):
  with members:
    - "exploiting first order regression in inductive policy selection"
    - "inductive policy selection for first order mdps"
    - "practical linear value approximation techniques for first order mdps"
    - "approximate policy iteration with a policy language bias solving relational markov decision processes"
    - "the automatic inference of state invariants in tim"
    - "learning first order definitions of functions"

Connected through hyperedge HE-7 (induced by source paper "first order decision diagrams for relational mdps"):
  with members:
    - "spudd stochastic planning using decision diagrams"
    - "exploiting first order regression in inductive policy selection"
    - "practical linear value approximation techniques for first order mdps"
    - "policy iteration for relational mdps"
    - "approximate linear programming for first order mdps"
    - "approximate policy iteration with a policy language bias solving relational markov decision processes"
    - "relational dynamic bayesian networks"
    - "efficient solution algorithms for factored mdps"

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)