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: "ordered landmarks in planning"
  Abstract: Many known planning tasks have inherent constraints concerning the best order in which to achieve the goals. A number of research efiorts have been made to detect such constraints and to use them for guiding search, in the hope of speeding up the planning process.#R##N##R##N#We go beyond the previous approaches by considering ordering constraints not only over the (top-level) goals, but also over the sub-goals that will necessarily arise during planning. Landmarks are facts that must be true at some point in every valid solution plan. We extend Koehler and Hoffmann's definition of reasonable orders between top level goals to the more general case of landmarks. We show how landmarks can be found, how their reasonable orders can be approximated, and how this information can be used to decompose a given planning task into several smaller sub-tasks. Our methodology is completely domain- and planner-independent. The implementation demonstrates that the approach can yield significant runtime performance improvements when used as a control loop around state-of-the-art sub-optimal planning systems, as exemplified by FF and LPG.

Connected through hyperedge HE-1 (induced by source paper "learn and link learning critical regions for efficient planning"):
  with members:
    - "learning sampling distributions for robot motion planning"
    - "u net convolutional networks for biomedical image segmentation"
    - "batch informed trees bit sampling based optimal planning via the heuristically guided search of implicit random geometric graphs"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"
    - "visualizing and understanding convolutional networks"

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 lama planner guiding cost based anytime planning with landmarks"
    - "the fast downward planning system"

Connected through hyperedge HE-3 (induced by source paper "identifying critical regions for motion planning using auto generated saliency labels with convolutional neural networks"):
  with members:
    - "learning sampling distributions for robot motion planning"
    - "u net convolutional networks for biomedical image segmentation"
    - "batch informed trees bit sampling based optimal planning via the heuristically guided search of implicit random geometric graphs"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"
    - "visualizing and understanding convolutional networks"

Connected through hyperedge HE-4 (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 lama planner guiding cost based anytime planning with landmarks"
    - "the fast downward planning system"
    - "the ff planning system fast plan generation through heuristic search"

Connected through hyperedge HE-5 (induced by source paper "why couldn t you do that explaining unsolvability of classical planning problems in the presence of plan advice"):
  with members:
    - "plan explanations as model reconciliation moving beyond explanation as soliloquy"

Connected through hyperedge HE-6 (induced by source paper "economics of human ai ecosystem value bias and lost utility in multi dimensional gaps"):
  with members:
    - "explaining explanations in ai"
    - "towards providing explanations for ai planner decisions"
    - "explainable security"
    - "explanation in artificial intelligence insights from the social sciences"
    - "what does explainable ai really mean a new conceptualization of perspectives"
    - "flexibly bounded rationality and marginalization of irrationality theories for decision making"
    - "soft goals can be compiled away"
    - "decision theoretic planning structural assumptions and computational leverage"

Connected through hyperedge HE-7 (induced by source paper "heuristic approaches for goal recognition in incomplete domain models"):
  with members:
    - "landmark based plan recognition"
    - "the lama planner guiding cost based anytime planning with landmarks"
    - "the ff planning system fast plan generation through heuristic search"

Connected through hyperedge HE-8 (induced by source paper "une approche totalement instanciee pour la planification htn"):
  with members:
    - "the fast downward planning system"
    - "the ff planning system fast plan generation through heuristic search"
    - "planning graph as a dynamic csp exploiting ebl ddb and other csp search techniques in graphplan"

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)