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: "balancing explicability and explanation in human aware planning"
  Abstract: Human aware planning requires an agent to be aware of the intentions, capabilities and mental model of the human in the loop during its decision process. This can involve generating plans that are explicable to a human observer as well as the ability to provide explanations when such plans cannot be generated. This has led to the notion "multi-model planning" which aim to incorporate effects of human expectation in the deliberative process of a planner - either in the form of explicable task planning or explanations produced thereof. In this paper, we bring these two concepts together and show how a planner can account for both these needs and achieve a trade-off during the plan generation process itself by means of a model-space search method MEGA. This in effect provides a comprehensive perspective of what it means for a decision making agent to be "human-aware" by bringing together existing principles of planning under the umbrella of a single plan generation process. We situate our discussion specifically keeping in mind the recent work on explicable planning and explanation generation, and illustrate these concepts in modified versions of two well known planning domains, as well as a demonstration on a robot involved in a typical search and reconnaissance task with an external supervisor.

Connected through hyperedge HE-1 (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"
    - "plan explanations as model reconciliation moving beyond explanation as soliloquy"

Connected through hyperedge HE-2 (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"
    - "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"
    - "plan explicability and predictability for robot task planning"

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"
    - "plan explanations as model reconciliation moving beyond explanation as soliloquy"
    - "explicable robot planning as minimizing distance from expected behavior"

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)