human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "interactive agent modeling by learning to probe"):
  Abstract: The ability of modeling the other agents, such as understanding their intentions and skills, is essential to an agent's interactions with other agents. Conventional agent modeling relies on passive observation from demonstrations. In this work, we propose an interactive agent modeling scheme enabled by encouraging an agent to learn to probe. In particular, the probing agent (i.e. a learner) learns to interact with the environment and with a target agent (i.e., a demonstrator) to maximize the change in the observed behaviors of that agent. Through probing, rich behaviors can be observed and are used for enhancing the agent modeling to learn a more accurate mind model of the target agent. Our framework consists of two learning processes: i) imitation learning for an approximated agent model and ii) pure curiosity-driven reinforcement learning for an efficient probing policy to discover new behaviors that otherwise can not be observed. We have validated our approach in four different tasks. The experimental results suggest that the agent model learned by our approach i) generalizes better in novel scenarios than the ones learned by passive observation, random probing, and other curiosity-driven approaches do, and ii) can be used for enhancing performance in multiple applications including distilling optimal planning to a policy net, collaboration, and competition. A video demo is available at this https URL

Includes members:
  Member "autonomous agents modelling other agents a comprehensive survey and open problems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the hanabi challenge a new frontier for ai research")
  Member "machine theory of mind":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "the next decade in ai four steps towards robust artificial intelligence")
  Member "one shot imitation from observing humans via domain adaptive meta learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "revisiting meta learning as supervised learning")
  Member "emergence of grounded compositional language in multi agent populations":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "improving coordination in small scale multi agent deep reinforcement learning through memory driven communication")
  Member "learned optimizers that scale and generalize":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "continuous adaptation via meta learning in nonstationary and competitive environments":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep reinforcement learning for autonomous driving a survey")
  Member "curiosity driven exploration by self supervised prediction":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deep reinforcement learning for autonomous driving a survey")
  Member "hierarchical and interpretable skill acquisition in multi task reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "the minerl competition on sample efficient reinforcement learning using human priors a retrospective")

Question: Please tell me which class the source paper 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)