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: "learning in real time search a unifying framework"
  Abstract: Real-time search methods are suited for tasks in which the agent is interacting with an initially unknown environment in real time. In such simultaneous planning and learning problems, the agent has to select its actions in a limited amount of time, while sensing only a local part of the environment centered at the agent's current location. Real-time heuristic search agents select actions using a limited lookahead search and evaluating the frontier states with a heuristic function. Over repeated experiences, they refine heuristic values of states to avoid infinite loops and to converge to better solutions. The wide spread of such settings in autonomous software and hardware agents has led to an explosion of real-time search algorithms over the last two decades. Not only is a potential user confronted with a hodgepodge of algorithms, but he also faces the choice of control parameters they use. In this paper we address both problems. The first contribution is an introduction of a simple three-parameter framework (named LRTS) which extracts the core ideas behind many existing algorithms. We then prove that LRTA*, E-LRTA* , SLA*, and γ-Trap algorithms are special cases of our framework. Thus, they are unified and extended with additional features. Second, we prove completeness and convergence of any algorithm covered by the LRTS framework. Third, we prove several upper-bounds relating the control parameters and solution quality. Finally, we analyze the influence of the three control parameters empirically in the realistic scalable domains of real-time navigation on initially unknown maps from a commercial role-playing game as well as routing in ad hoc sensor networks.

Connected through hyperedge HE-1 (induced by source paper "multi step greedy and approximate real time dynamic programming"):
  with members:
    - "tight regret bounds for model based reinforcement learning with greedy policies"
    - "how to combine tree search methods in reinforcement learning"
    - "near optimal behavior via approximate state abstraction"
    - "variance reduced value iteration and faster algorithms for solving markov decision processes"
    - "unifying pac and regret uniform pac bounds for episodic reinforcement learning"
    - "model reduction techniques for computing approximately optimal solutions for markov decision processes"

Connected through hyperedge HE-2 (induced by source paper "mean based heuristic search for real time planning"):
  with members:
    - "learning for adaptive real time search"
    - "the ff planning system fast plan generation through heuristic search"

Connected through hyperedge HE-3 (induced by source paper "search space characterization for real time heuristic search"):
  with members:
    - "case based subgoaling in real time heuristic search for video game pathfinding"

Connected through hyperedge HE-4 (induced by source paper "avoiding and escaping depressions in real time heuristic search"):
  with members:
    - "cost based satisficing search considered harmful"
    - "case based subgoaling in real time heuristic search for video game pathfinding"
    - "where ignoring delete lists works local search topology in planning benchmarks"
    - "learning for adaptive real time search"

Connected through hyperedge HE-5 (induced by source paper "case based subgoaling in real time heuristic search for video game pathfinding"):
  with members:
    - "learning for adaptive real time search"

Connected through hyperedge HE-6 (induced by source paper "on backtracking in real time heuristic search"):
  with members:
    - "learning for adaptive real time search"

Connected through hyperedge HE-7 (induced by source paper "adaptive stochastic resource control a machine learning approach"):
  with members:
    - "resource allocation among agents with mdp induced preferences"

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)