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 "from abstractions to natural languages for coordinating planning agents"):
  Abstract: Despite significant advancements in developing autonomous agents, communication between them often relies on a set of pre-specified symbols for a given domain. In this paper, we investigate the automatic construction of these symbols from abstractions to form "natural languages" for such agents. The focus of this initial investigation is on a task planning setting where one agent (the speaker) directly communicates a "plan sketch" to another agent (the listener) to achieve coordination. In contrast to prior work, we view language formation as a fundamental requirement for resolving miscoordination. This view enables us to "compute" a language from the ground up by mapping physical states to symbols, thus reverse engineering the function of languages. Languages that arise from this process are only approximately expressive of the actual plans, meaning that they specify abstractions over the plan space, which is not only theoretically appealing as it provides the desired flexibility to the listener to choose its plan during execution, but also practically useful since it both reduces the communication cost of the speaker and computational cost of the listener. We formulate this language construction problem and show that it is NEXP-complete. An approximate solution is then developed to relate this problem to task planning problems that have efficient off-the-shelf solutions. Finally, we discuss a multi-agent path-finding domain in our evaluation to provide a comprehensive set of results to illustrate the benefits of the constructed languages and their applications.

Includes members:
  Member "translating neuralese":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "countering language drift via visual grounding")
  Member "near optimal behavior via approximate state abstraction":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning state abstractions for transfer in continuous control")
  Member "emergence of grounded compositional language in multi agent populations":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "improving coordination in small scale multi agent deep reinforcement learning through memory driven communication")
  Member "learning multiagent communication with backpropagation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "review analyze and design a comprehensive deep reinforcement learning framework")
  Member "a paradigm for situated and goal driven language learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multi agent reinforcement learning as a computational tool for language evolution research historical context and future challenges")
  Member "synthesizing robust plans under incomplete domain models":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "towards evaluating plan generation approaches with instructional texts")
  Member "the symbol grounding problem":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "in defense of grid features for visual question answering")

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)