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 explanations as model reconciliation moving beyond explanation as soliloquy"
  Abstract: When AI systems interact with humans in the loop, they are often called on to provide explanations for their plans and behavior. Past work on plan explanations primarily involved the AI system explaining the correctness of its plan and the rationale for its decision in terms of its own model. Such soliloquy is wholly inadequate in most realistic scenarios where the humans have domain and task models that differ significantly from that used by the AI system. We posit that the explanations are best studied in light of these differing models. In particular, we show how explanation can be seen as a "model reconciliation problem" (MRP), where the AI system in effect suggests changes to the human's model, so as to make its plan be optimal with respect to that changed human model. We will study the properties of such explanations, present algorithms for automatically computing them, and evaluate the performance of the algorithms.

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 "explainable agents through social cues a review"):
  with members:
    - "explainability in human agent systems"
    - "explainable artificial intelligence xai concepts taxonomies opportunities and challenges toward responsible ai"
    - "interactively shaping robot behaviour with unlabeled human instructions"
    - "expressing robot incapability"
    - "explicability legibility predictability transparency privacy security the emerging landscape of interpretable agent behavior"
    - "expressive robot motion timing"
    - "enabling robots to communicate their objectives"
    - "balancing explicability and explanation in human aware planning"

Connected through hyperedge HE-3 (induced by source paper "why not give this work to them explaining ai moderated task allocation outcomes using negotiation trees"):
  with members:
    - "negotiation strategies for agents with ordinal preferences"
    - "towards providing explanations for ai planner decisions"
    - "explanation in artificial intelligence insights from the social sciences"
    - "why should i trust you explaining the predictions of any classifier"
    - "negotiating socially optimal allocations of resources"

Connected through hyperedge HE-4 (induced by source paper "d3ba a tool for optimizing business processes using non deterministic planning"):
  with members:
    - "a unified conversational assistant framework for business process automation"
    - "planning for goal oriented dialogue systems"
    - "generating dialogue agents via automated planning"

Connected through hyperedge HE-5 (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 explicability and predictability for robot task planning"

Connected through hyperedge HE-6 (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"
    - "a refinement based architecture for knowledge representation and reasoning in robotics"
    - "plan explicability and predictability for robot task planning"

Connected through hyperedge HE-7 (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"
    - "discovering underlying plans based on distributed representations of actions"
    - "plan explicability and predictability for robot task planning"

Connected through hyperedge HE-8 (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"
    - "explicablility as minimizing distance from expected behavior"
    - "plan explicability and predictability for robot task planning"
    - "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)