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: "explicable robot planning as minimizing distance from expected behavior"
  Abstract: In order for robots to be integrated effectively into human work-flows, it is not enough to address the question of autonomy but also how their actions or plans are being perceived by their human counterparts. When robots generate task plans without such considerations, they may often demonstrate what we refer to as inexplicable behavior from the point of view of humans who may be observing it. This problem arises due to the human observer's partial or inaccurate understanding of the robot's deliberative process and/or the model (i.e. capabilities of the robot) that informs it. This may have serious implications on the human-robot work-space, from increased cognitive load and reduced trust in the robot from the human, to more serious concerns of safety in human-robot interactions. In this paper, we propose to address this issue by learning a distance function that can accurately model the notion of explicability, and develop an anytime search algorithm that can use this measure in its search process to come up with progressively explicable plans. As the first step, robot plans are evaluated by human subjects based on how explicable they perceive the plan to be, and a scoring function called explicability distance based on the different plan distance measures is learned. We then use this explicability distance as a heuristic to guide our search in order to generate explicable robot plans, by minimizing the plan distances between the robot's plan and the human's expected plans. We conduct our experiments in a toy autonomous car domain, and provide empirical evaluations that demonstrate the usefulness of the approach in making the planning process of an autonomous agent conform to human expectations.

Connected through hyperedge HE-1 (induced by source paper "expectation aware planning a unifying framework for synthesizing and executing self explaining plans for human aware planning"):
  with members:
    - "explanation in artificial intelligence insights from the social sciences"
    - "a general multi agent epistemic planner based on higher order belief change"
    - "plan explanations as model reconciliation moving beyond explanation as soliloquy"
    - "plan explicability and predictability for robot task planning"

Connected through hyperedge HE-2 (induced by source paper "to monitor or to trust observing robot s behavior based on a game theoretic model of trust"):
  with members:
    - "the off switch game"
    - "plan explicability and predictability for robot task planning"
    - "the fast downward planning system"

Connected through hyperedge HE-3 (induced by source paper "plan explanations as model reconciliation an empirical study"):
  with members:
    - "explanation in artificial intelligence insights from the social sciences"
    - "balancing explicability and explanation in human aware planning"
    - "plan explanations as model reconciliation moving beyond explanation as soliloquy"

Connected through hyperedge HE-4 (induced by source paper "balancing explicability and explanation in human aware planning"):
  with members:
    - "ai challenges in human robot cognitive teaming"
    - "plan explanations as model reconciliation moving beyond explanation as soliloquy"
    - "cooperative inverse reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "plan explanations as model reconciliation moving beyond explanation as soliloquy"):
  with members:
    - "plan explainability and predictability for cobots"
    - "the fast downward planning system"

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)