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: "a heuristic search approach to planning with continuous resources in stochastic domains"
  Abstract: We consider the problem of optimal planning in stochastic domains with resource constraints, where the resources are continuous and the choice of action at each step depends on resource availability. We introduce the HAO* algorithm, a generalization of the AO* algorithm that performs search in a hybrid state space that is modeled using both discrete and continuous state variables, where the continuous variables represent monotonic resources. Like other heuristic search algorithms, HAO* leverages knowledge of the start state and an admissible heuristic to focus computational effort on those parts of the state space that could be reached from the start state by following an optimal policy. We show that this approach is especially effective when resource constraints limit how much of the state space is reachable. Experimental results demonstrate its effectiveness in the domain that motivates our research: automated planning for planetary exploration rovers.

Connected through hyperedge HE-1 (induced by source paper "bounded approximate symbolic dynamic programming for hybrid mdps"):
  with members:
    - "dynamic programming for structured continuous markov decision problems"
    - "planning under continuous time and resource uncertainty a challenge for ai"
    - "solving factored mdps with hybrid state and action variables"
    - "decision theoretic planning structural assumptions and computational leverage"

Connected through hyperedge HE-2 (induced by source paper "colin planning with continuous linear numeric change"):
  with members:
    - "the lama planner guiding cost based anytime planning with landmarks"
    - "compiling uncertainty away in conformant planning problems with bounded width"
    - "modelling mixed discrete continuous domains for planning"
    - "the fast downward planning system"
    - "an approach to temporal planning and scheduling in domains with predictable exogenous events"
    - "the deterministic part of ipc 4 an overview"
    - "sapa a multi objective metric temporal planner"
    - "taming numbers and durations in the model checking integrated planning system"

Connected through hyperedge HE-3 (induced by source paper "symbolic dynamic programming for discrete and continuous state mdps"):
  with members:
    - "spudd stochastic planning using decision diagrams"
    - "dynamic programming for structured continuous markov decision problems"
    - "planning under continuous time and resource uncertainty a challenge for ai"
    - "solving factored mdps with hybrid state and action variables"
    - "decision theoretic planning structural assumptions and computational leverage"
    - "exploiting causal independence in bayesian network inference"

Connected through hyperedge HE-4 (induced by source paper "plan based policies for efficient multiple battery load management"):
  with members:
    - "modelling mixed discrete continuous domains for planning"
    - "approximate policy iteration with a policy language bias solving relational markov decision processes"

Connected through hyperedge HE-5 (induced by source paper "topological value iteration algorithms"):
  with members:
    - "on the complexity of solving markov decision problems"
    - "flexible decomposition algorithms for weakly coupled markov decision problems"
    - "hierarchical solution of markov decision processes using macro actions"
    - "the complexity of decentralized control of markov decision processes"
    - "spudd stochastic planning using decision diagrams"
    - "region based incremental pruning for pomdps"
    - "planning under continuous time and resource uncertainty a challenge for ai"
    - "symbolic generalization for on line planning"

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)