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: "a unified framework for planning in adversarial and cooperative environments"
  Abstract: Users of AI systems may rely upon them to produce plans for achieving desired objectives. Such AI systems should be able to compute obfuscated plans whose execution in adversarial situations protects privacy, as well as legible plans which are easy for team members to understand in cooperative situations. We develop a unified framework that addresses these dual problems by computing plans with a desired level of comprehensibility from the point of view of a partially informed observer. For adversarial settings, our approach produces obfuscated plans with observations that are consistent with at least k goals from a set of decoy goals. By slightly varying our framework, we present an approach for goal legibility in cooperative settings which produces plans that achieve a goal while being consistent with at most j goals from a set of confounding goals. In addition, we show how the observability of the observer can be controlled to either obfuscate or clarify the next actions in a plan when the goal is known to the observer. We present theoretical results on the complexity analysis of our problems. We demonstrate the execution of obfuscated and legible plans in a cooking domain using a physical robot Fetch. We also provide an empirical evaluation to show the feasibility and usefulness of our approaches using IPC domains.

Connected through hyperedge HE-1 (induced by source paper "signaling friends and head faking enemies simultaneously balancing goal obfuscation and goal legibility"):
  with members:
    - "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 "landmark based approaches for goal recognition as planning"):
  with members:
    - "autonomous agents modelling other agents a comprehensive survey and open problems"
    - "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"
    - "multiple goal heuristic search"

Connected through hyperedge HE-3 (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"
    - "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"
    - "plan explicability and predictability for robot task planning"

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)