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: "plan explicability and predictability for robot task planning"
  Abstract: Intelligent robots and machines are becoming pervasive in human populated environments. A desirable capability of these agents is to respond to goal-oriented commands by autonomously constructing task plans. However, such autonomy can add significant cognitive load and potentially introduce safety risks to humans when agents behave unexpectedly. Hence, for such agents to be helpful, one important requirement is for them to synthesize plans that can be easily understood by humans. While there exists previous work that studied socially acceptable robots that interact with humans in "natural ways", and work that investigated legible motion planning, there lacks a general solution for high level task planning. To address this issue, we introduce the notions of plan {\it explicability} and {\it predictability}. To compute these measures, first, we postulate that humans understand agent plans by associating abstract tasks with agent actions, which can be considered as a labeling process. We learn the labeling scheme of humans for agent plans from training examples using conditional random fields (CRFs). Then, we use the learned model to label a new plan to compute its explicability and predictability. These measures can be used by agents to proactively choose or directly synthesize plans that are more explicable and predictable to humans. We provide evaluations on a synthetic domain and with human subjects using physical robots to show the effectiveness of our approach

Connected through hyperedge HE-1 (induced by source paper "the emerging landscape of explainable ai planning and decision making"):
  with members:
    - "explainable reinforcement learning through a causal lens"
    - "generation of policy level explanations for reinforcement learning"
    - "expectation aware planning a unifying framework for synthesizing and executing self explaining plans for human aware planning"
    - "towards explainable ai planning as a service"
    - "exploring computational user models for agent policy summarization"
    - "explicability legibility predictability transparency privacy security the emerging landscape of interpretable agent behavior"
    - "contrastive explanation a structural model approach"
    - "learning finite state representations of recurrent policy networks"

Connected through hyperedge HE-2 (induced by source paper "signaling friends and head faking enemies simultaneously balancing goal obfuscation and goal legibility"):
  with members:
    - "a unified framework for planning in adversarial and cooperative environments"
    - "plan explanations as model reconciliation moving beyond explanation as soliloquy"

Connected through hyperedge HE-3 (induced by source paper "explanation in human ai systems a literature meta review synopsis of key ideas and publications and bibliography for explainable ai"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "ted teaching ai to explain its decisions"
    - "explaining explanations an overview of interpretability of machine learning"
    - "deep learning a critical appraisal"
    - "how do humans understand explanations from machine learning systems an evaluation of the human interpretability of explanation"
    - "explainable artificial intelligence xai the goodness criteria and the grasp ability test"
    - "interpretable explorable approximations of black box models"
    - "explaining the unexplained a class enhanced attentive response clear approach to understanding deep neural networks"

Connected through hyperedge HE-4 (induced by source paper "motion reasoning for goal based imitation learning"):
  with members:
    - "representing robot task plans as robust logical dynamical systems"
    - "d3tw discriminative differentiable dynamic time warping for weakly supervised action alignment and segmentation"
    - "dexpilot vision based teleoperation of dexterous robotic hand arm system"
    - "scaling egocentric vision the epic kitchens dataset"
    - "explicability legibility predictability transparency privacy security the emerging landscape of interpretable agent behavior"
    - "zero shot visual imitation"
    - "neural task graphs generalizing to unseen tasks from a single video demonstration"
    - "beyond imitation zero shot task transfer on robots by learning concepts as cognitive programs"

Connected through hyperedge HE-5 (induced by source paper "desiderata for planning systems in general purpose service robots"):
  with members:
    - "what should i do now marrying reinforcement learning and symbolic planning"
    - "improving grounded natural language understanding through human robot dialog"
    - "interaction and autonomy in robocup home and building wide intelligence"
    - "integrating task motion planning with reinforcement learning for robust decision making in mobile robots"
    - "balancing explicability and explanation in human aware planning"
    - "the strands project long term autonomy in everyday environments"
    - "plan explanations as model reconciliation moving beyond explanation as soliloquy"
    - "a refinement based architecture for knowledge representation and reasoning in robotics"

Connected through hyperedge HE-6 (induced by source paper "challenges of human aware ai systems"):
  with members:
    - "planning with explanatory actions a joint approach to plan explicability and explanations in human aware planning"
    - "explicability legibility predictability transparency privacy security the emerging landscape of interpretable agent behavior"
    - "plan explanations as model reconciliation an empirical study"
    - "a unified framework for planning in adversarial and cooperative environments"
    - "recognizing plans by learning embeddings from observed action distributions"
    - "ai challenges in human robot cognitive teaming"
    - "plan explanations as model reconciliation moving beyond explanation as soliloquy"
    - "discovering underlying plans based on distributed representations of actions"

Connected through hyperedge HE-7 (induced by source paper "online explanation generation for human robot teaming"):
  with members:
    - "progressive explanation generation for human robot teaming"
    - "where do you think you re going inferring beliefs about dynamics from behavior"
    - "explanation in artificial intelligence insights from the social sciences"
    - "ai challenges in human robot cognitive teaming"
    - "plan explanations as model reconciliation moving beyond explanation as soliloquy"
    - "explicablility as minimizing distance from expected behavior"
    - "pddl2 1 an extension to pddl for expressing temporal planning domains"

Connected through hyperedge HE-8 (induced by source paper "progressive explanation generation for human robot teaming"):
  with members:
    - "hierarchical expertise level modeling for user specific robot behavior explanations"
    - "explanation in artificial intelligence insights from the social sciences"
    - "ai challenges in human robot cognitive teaming"
    - "plan explanations as model reconciliation moving beyond explanation as soliloquy"
    - "the fast downward planning system"
    - "pddl2 1 an extension to pddl for expressing temporal planning domains"

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)