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 fast downward planning system"
  Abstract: Fast Downward is a classical planning system based on heuristic search. It can deal with general deterministic planning problems encoded in the propositional fragment of PDDL2.2, including advanced features like ADL conditions and effects and derived predicates (axioms). Like other well-known planners such as HSP and FF, Fast Downward is a progression planner, searching the space of world states of a planning task in the forward direction. However, unlike other PDDL planning systems, Fast Downward does not use the propositional PDDL representation of a planning task directly. Instead, the input is first translated into an alternative representation called multivalued planning tasks, which makes many of the implicit constraints of a propositional planning task explicit. Exploiting this alternative representation, Fast Downward uses hierarchical decompositions of planning tasks for computing its heuristic function, called the causal graph heuristic, which is very different from traditional HSP-like heuristics based on ignoring negative interactions of operators.#R##N##R##N#In this article, we give a full account of Fast Downward's approach to solving multivalued planning tasks. We extend our earlier discussion of the causal graph heuristic to tasks involving axioms and conditional effects and present some novel techniques for search control that are used within Fast Downward's best-first search algorithm: preferred operators transfer the idea of helpful actions from local search to global best-first search, deferred evaluation of heuristic functions mitigates the negative effect of large branching factors on search performance, and multiheuristic best-first search combines several heuristic evaluation functions within a single search algorithm in an orthogonal way. We also describe efficient data structures for fast state expansion (successor generators and axiom evaluators) and present a new non-heuristic search algorithm called focused iterative-broadening search, which utilizes the information encoded in causal graphs in a novel way.#R##N##R##N#Fast Downward has proven remarkably successful: It won the "classical" (i. e., propositional, non-optimising) track of the 4th International Planning Competition at ICAPS 2004, following in the footsteps of planners such as FF and LPG. Our experiments show that it also performs very well on the benchmarks of the earlier planning competitions and provide some insights about the usefulness of the new search enhancements.

Connected through hyperedge HE-1 (induced by source paper "transferable task execution from pixels through deep planning domain learning"):
  with members:
    - "representing robot task plans as robust logical dynamical systems"
    - "prospection interpretable plans from language by predicting the future"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "neural task graphs generalizing to unseen tasks from a single video demonstration"
    - "stripstream integrating symbolic planners and blackbox samplers"
    - "peorl integrating symbolic planning and hierarchical reinforcement learning for robust decision making"
    - "one shot hierarchical imitation learning of compound visuomotor tasks"
    - "visual robot task planning"

Connected through hyperedge HE-2 (induced by source paper "batch informed trees bit informed asymptotically optimal anytime search"):
  with members:
    - "informed sampling for asymptotically optimal path planning"
    - "incremental sampling based motion planners using policy iteration methods"
    - "deterministic sampling based motion planning optimality complexity and performance"
    - "batch informed trees bit sampling based optimal planning via the heuristically guided search of implicit random geometric graphs"
    - "informed rrt optimal sampling based path planning focused via direct sampling of an admissible ellipsoidal heuristic"
    - "bit batch informed trees for optimal sampling based planning via dynamic programming on implicit random geometric graphs"
    - "asymptotically optimal motion planning using lower bounds on cost"
    - "efficient high quality motion planning by fast all pairs r nearest neighbors"

Connected through hyperedge HE-3 (induced by source paper "scalable and probabilistically complete planning for robotic spatial extrusion"):
  with members:
    - "freeform assembly planning"
    - "automated sequence and motion planning for robotic spatial extrusion of 3d trusses"
    - "sampling based methods for factored task and motion planning"
    - "probabilistic completeness of rrt for geometric and kinodynamic planning with forward propagation"
    - "complexity results and fast methods for optimal tabletop rearrangement with overhand grasps"
    - "backward forward search for manipulation planning"
    - "sampling based algorithms for optimal motion planning"

Connected through hyperedge HE-4 (induced by source paper "object centric task and motion planning in dynamic environments"):
  with members:
    - "continuous relaxation of symbolic planner for one shot imitation learning"
    - "sampling based methods for factored task and motion planning"
    - "combined task and motion planning as classical ai planning"
    - "depth based object tracking using a robust gaussian filter"
    - "probabilistic object tracking using a range camera"
    - "towards combining htn planning and geometric task planning"

Connected through hyperedge HE-5 (induced by source paper "strips action discovery"):
  with members:
    - "learning strips action models with classical planning"
    - "learning first order symbolic representations for planning from the structure of the state space"
    - "classical planning in deep latent space bridging the subsymbolic symbolic boundary"
    - "learning strips operators from noisy and incomplete observations"
    - "learning partially observable deterministic action models"

Connected through hyperedge HE-6 (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-7 (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 lama planner guiding cost based anytime planning with landmarks"
    - "multiple goal heuristic search"

Connected through hyperedge HE-8 (induced by source paper "representing robot task plans as robust logical dynamical systems"):
  with members:
    - "behavior trees in robotics and ai an introduction"
    - "behavior trees as a representation for medical procedures"
    - "neural task graphs generalizing to unseen tasks from a single video demonstration"
    - "rmpflow a computational graph for automatic motion policy generation"
    - "evaluating methods for end user creation of robot task plans"
    - "combining neural networks and tree search for task and motion planning in challenging environments"
    - "neural task programming learning to generalize across hierarchical tasks"
    - "posecnn a convolutional neural network for 6d object pose estimation in cluttered scenes"

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)