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: "the lama planner guiding cost based anytime planning with landmarks"
  Abstract: LAMA is a classical planning system based on heuristic forward search. Its core feature is the use of a pseudo-heuristic derived from landmarks, propositional formulas that must be true in every solution of a planning task. LAMA builds on the Fast Downward planning system, using finite-domain rather than binary state variables and multi-heuristic search. The latter is employed to combine the landmark heuristic with a variant of the well-known FF heuristic. Both heuristics are cost-sensitive, focusing on high-quality solutions in the case where actions have non-uniform cost. A weighted A* search is used with iteratively decreasing weights, so that the planner continues to search for plans of better quality until the search is terminated.#R##N##R##N#LAMA showed best performance among all planners in the sequential satisficing track of the International Planning Competition 2008. In this paper we present the system in detail and investigate which features of LAMA are crucial for its performance. We present individual results for some of the domains used at the competition, demonstrating good and bad cases for the techniques implemented in LAMA. Overall, we find that using landmarks improves performance, whereas the incorporation of action costs into the heuristic estimators proves not to be beneficial. We show that in some domains a search that ignores cost solves far more problems, raising the question of how to deal with action costs more effectively in the future. The iterated weighted A* search greatly improves results, and shows synergy effects with the use of landmarks.

Connected through hyperedge HE-1 (induced by source paper "learning domain independent planning heuristics with hypergraph networks"):
  with members:
    - "asnets deep learning for generalised planning"
    - "size independent neural transfer for rddl planning"
    - "relational inductive biases deep learning and graph networks"
    - "relational inductive bias for physical construction in humans and machines"
    - "learning generalized reactive policies using deep neural networks"
    - "action schema networks generalised policies with deep learning"
    - "towards learning domain independent planning heuristics"
    - "neural message passing for quantum chemistry"

Connected through hyperedge HE-2 (induced by source paper "landmark based approaches for goal recognition as planning"):
  with members:
    - "autonomous agents modelling other agents a comprehensive survey and open problems"
    - "a unified framework for planning in adversarial and cooperative environments"
    - "heuristic online goal recognition in continuous domains"
    - "session analysis using plan recognition"
    - "landmark based plan recognition"
    - "accounting for context in plan recognition with application to traffic monitoring"
    - "the fast downward planning system"
    - "multiple goal heuristic search"

Connected through hyperedge HE-3 (induced by source paper "learning neural search policies for classical planning"):
  with members:
    - "learning classical planning strategies with policy gradient"
    - "improving exploration in evolution strategies for deep reinforcement learning via a population of novelty seeking agents"
    - "learning to rank for synthesizing planning heuristics"
    - "marvin a heuristic search planner with online macro action learning"
    - "the fast downward planning system"
    - "the ff planning system fast plan generation through heuristic search"

Connected through hyperedge HE-4 (induced by source paper "generalized planning with procedural domain control knowledge"):
  with members:
    - "scaling up heuristic planning with relational decision trees"
    - "compiling uncertainty away in conformant planning problems with bounded width"
    - "marvin a heuristic search planner with online macro action learning"
    - "the fast downward planning system"
    - "macro ff improving ai planning with automatically learned macro operators"

Connected through hyperedge HE-5 (induced by source paper "what you get is what you see decomposing epistemic planning using functional strips"):
  with members:
    - "a gentle introduction to epistemic planning the del approach"
    - "cooperative epistemic multi agent planning for implicit coordination"
    - "a general multi agent epistemic planner based on higher order belief change"
    - "the arcade learning environment an evaluation platform for general agents"
    - "the fast downward planning system"

Connected through hyperedge HE-6 (induced by source paper "using sub optimal plan detection to identify commitment abandonment in discrete environments"):
  with members:
    - "higher dimensional potential heuristics for optimal classical planning"
    - "landmark based plan recognition"
    - "the fast downward planning system"
    - "ordered landmarks in planning"
    - "the ff planning system fast plan generation through heuristic search"

Connected through hyperedge HE-7 (induced by source paper "timeline based planning and execution with uncertainty theory modeling methodologies and practice"):
  with members:
    - "hatp an htn planner for robotics"
    - "colin planning with continuous linear numeric change"
    - "pddl2 1 an extension to pddl for expressing temporal planning domains"
    - "the ff planning system fast plan generation through heuristic search"

Connected through hyperedge HE-8 (induced by source paper "solving multiagent planning problems with concurrent conditional effects"):
  with members:
    - "fmap distributed cooperative multi agent planning"
    - "the fast downward planning system"
    - "pddl2 1 an extension to pddl for expressing temporal planning domains"
    - "partial order planning with concurrent interacting actions"

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)