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 theory of goal oriented mdps with dead ends"
  Abstract: Stochastic Shortest Path (SSP) MDPs is a problem class widely studied in AI, especially in probabilistic planning. They describe a wide range of scenarios but make the restrictive assumption that the goal is reachable from any state, i.e., that dead-end states do not exist. Because of this, SSPs are unable to model various scenarios that may have catastrophic events (e.g., an airplane possibly crashing if it flies into a storm). Even though MDP algorithms have been used for solving problems with dead ends, a principled theory of SSP extensions that would allow dead ends, including theoretically sound algorithms for solving such MDPs, has been lacking. In this paper, we propose three new MDP classes that admit dead ends under increasingly weaker assumptions. We present Value Iteration-based as well as the more efficient heuristic search algorithms for optimally solving each class, and explore theoretical relationships between these classes. We also conduct a preliminary empirical study comparing the performance of our algorithms on different MDP classes, especially on scenarios with unavoidable dead ends.

Connected through hyperedge HE-1 (induced by source paper "point based methods for model checking in partially observable markov decision processes"):
  with members:
    - "verification of uncertain pomdps using barrier certificates"
    - "bounded policy synthesis for pomdps with safe reachability objectives"
    - "a storm is coming a modern probabilistic model checker"
    - "inverse reward design"
    - "qualitative analysis of pomdps with temporal logic specifications for robotics applications"
    - "a learning based approach to control synthesis of markov decision processes for linear temporal logic specifications"
    - "what is decidable about partially observable markov decision processes with omega regular objectives"
    - "heuristic search value iteration for pomdps"

Connected through hyperedge HE-2 (induced by source paper "size independent neural transfer for rddl planning"):
  with members:
    - "transfer of deep reactive policies for mdp planning"
    - "learning generalized reactive policies using deep neural networks"
    - "action schema networks generalised policies with deep learning"
    - "asynchronous methods for deep reinforcement learning"
    - "value iteration networks"

Connected through hyperedge HE-3 (induced by source paper "transfer of deep reactive policies for mdp planning"):
  with members:
    - "graph embedding techniques applications and performance a survey"
    - "teacher student curriculum learning"
    - "learning generalized reactive policies using deep neural networks"
    - "towards deep symbolic reinforcement learning"
    - "value iteration networks"
    - "attend adapt and transfer attentive deep architecture for adaptive transfer from multiple sources in the same domain"
    - "fast and accurate deep network learning by exponential linear units elus"

Connected through hyperedge HE-4 (induced by source paper "sensor synthesis for pomdps with reachability objectives"):
  with members:
    - "a mathematical theory of co design"
    - "a symbolic sat based algorithm for almost sure reachability with small strategies in pomdps"
    - "qualitative analysis of pomdps with temporal logic specifications for robotics applications"
    - "heuristic search value iteration for pomdps"
    - "qualitative analysis of partially observable markov decision processes"
    - "algorithms for omega regular games with imperfect information"
    - "reinforcement learning a survey"

Connected through hyperedge HE-5 (induced by source paper "gambler s ruin bandit problem"):
  with members:
    - "releaf an algorithm for learning and exploiting relevance"
    - "analysis of thompson sampling for the multi armed bandit problem"

Connected through hyperedge HE-6 (induced by source paper "optimal cost almost sure reachability in pomdps"):
  with members:
    - "heuristic search value iteration for pomdps"
    - "suboptimality bounds for stochastic shortest path problems"
    - "nonapproximability results for partially observable markov decision processes"
    - "reinforcement learning a survey"

Connected through hyperedge HE-7 (induced by source paper "a survey of multi objective sequential decision making"):
  with members:
    - "markov decision processes with multiple long run averageobjectives"
    - "approximation of lorenz optimal solutions in multiobjective markov decision processes"
    - "incremental pruning a simple fast exact method for partially observable markov decision processes"
    - "pareto curves for probabilistic model checking"
    - "online planning algorithms for pomdps"
    - "risk sensitive reinforcement learning applied to control under constraints"
    - "perseus randomized point based value iteration for pomdps"
    - "an evolutionary algorithm with advanced goal and priority specification for multi objective optimization"

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)