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: "first order decision diagrams for relational mdps"
  Abstract: Markov decision processes capture sequential decision making under uncertainty, where an agent must choose actions so as to optimize long term reward. The paper studies efficient reasoning mechanisms for Relational Markov Decision Processes (RMDP) where world states have an internal relational structure that can be naturally described in terms of objects and relations among them. Two contributions are presented. First, the paper develops First Order Decision Diagrams (FODD), a new compact representation for functions over relational structures, together with a set of operators to combine FODDs, and novel reduction techniques to keep the representation small. Second, the paper shows how FODDs can be used to develop solutions for RMDPs, where reasoning is performed at the abstract level and the resulting optimal policy is independent of domain size (number of objects) or instantiation. In particular, a variant of the value iteration algorithm is developed by using special operations over FODDs, and the algorithm is shown to converge to the optimal policy.

Connected through hyperedge HE-1 (induced by source paper "qualitative numeric planning reductions and complexity"):
  with members:
    - "transfer of deep reactive policies for mdp planning"
    - "compact policies for fully observable non deterministic planning as sat"
    - "features projections and representation change for generalized planning"
    - "learning features and abstract actions for computing generalized plans"
    - "learning generalized reactive policies using deep neural networks"
    - "action schema networks generalised policies with deep learning"
    - "mazebase a sandbox for learning from games"
    - "the computational complexity of probabilistic planning"

Connected through hyperedge HE-2 (induced by source paper "guarantees for sound abstractions for generalized planning extended paper"):
  with members:
    - "generalized planning non deterministic abstractions and trajectory constraints"
    - "features projections and representation change for generalized planning"
    - "learning features and abstract actions for computing generalized plans"
    - "the automatic inference of state invariants in tim"

Connected through hyperedge HE-3 (induced by source paper "state space abstractions for probabilistic inference a systematic review"):
  with members:
    - "stochastic planning and lifted inference"
    - "domain recursion for lifted inference with existential quantifiers"
    - "contextual symmetries in probabilistic graphical models"
    - "weighted positive binary decision diagrams for exact probabilistic inference"
    - "lifted probabilistic inference for asymmetric graphical models"
    - "lifted tree reweighted variational inference"
    - "on the complexity and approximation of binary evidence in lifted inference"
    - "symmetry aware marginal density estimation"

Connected through hyperedge HE-4 (induced by source paper "learning features and abstract actions for computing generalized plans"):
  with members:
    - "composable planning with attributes"
    - "features projections and representation change for generalized planning"
    - "representation learning on graphs methods and applications"
    - "towards deep symbolic reinforcement learning"
    - "mazebase a sandbox for learning from games"
    - "approximate policy iteration with a policy language bias solving relational markov decision processes"

Connected through hyperedge HE-5 (induced by source paper "features projections and representation change for generalized planning"):
  with members:
    - "generalized planning non deterministic abstractions and trajectory constraints"
    - "learning generalized reactive policies using deep neural networks"
    - "learning to navigate in complex environments"
    - "approximate policy iteration with a policy language bias solving relational markov decision processes"

Connected through hyperedge HE-6 (induced by source paper "stochastic planning and lifted inference"):
  with members:
    - "the complexity of reasoning with fodd and gfodd"
    - "solving relational mdps with exogenous events and additive rewards"
    - "spudd stochastic planning using decision diagrams"
    - "belief propagation for structured decision making"
    - "policy iteration for relational mdps"
    - "decision theoretic planning structural assumptions and computational leverage"

Connected through hyperedge HE-7 (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"
    - "flucap a heuristic search planner for first order mdps"

Connected through hyperedge HE-8 (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"
    - "flucap a heuristic search planner for first order mdps"
    - "efficient solution algorithms for factored mdps"
    - "decision theoretic planning structural assumptions and computational leverage"

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)