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 "generalized grounding graphs a probabilistic framework for understanding grounded commands"):
  Abstract: Many task domains require robots to interpret and act upon natural language commands which are given by people and which refer to the robot’s physical surroundings. Such interpretation, known as the “grounded language problem,” is challenging both because people, especially untrained users, employ diverse vocabulary and grammar, and because in real settings robots typically have substantial uncertainty about the nature and contents of their surroundings, making it difficult to associate the constitutive language elements (principally noun phrases and spatial relations) of the command text to elements of those surroundings. Symbolic models capture linguistic structure but have not scaled successfully to handle the diverse language produced by untrained users. Existing statistical approaches can better handle diversity, but have not to date modeled complex linguistic structure, limiting achievable accuracy. Recent hybrid approaches have addressed limitations in scaling and complexity, but have not effectively associated linguistic and perceptual features. Our framework, called Generalized Grounding Graphs (G 3 ), addresses these issues by defining a probabilistic graphical model dynamically according to the linguistic parse structure of a natural language command. This approach scales effectively, handles linguistic diversity, and enables the system to associate parts of a command with the specific objects, places, and events in the external world to which they refer. This enables robots to learn word meanings and use those learned meanings to robustly follow natural language commands produced by untrained users. We demonstrate our approach for both mobility commands (e.g. route directions like “Go down the hallway through the door”) and mobile manipulation commands (e.g. physical directives like “Pick up the pallet on the truck”) involving a variety of semi-autonomous robotic platforms, including a wheelchair, a micro-air vehicle, a forklift, and the Willow Garage PR2.

Includes members:
  Member "learning to win by reading manuals in a monte carlo framework":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a survey of reinforcement learning informed by natural language")
  Member "learning to map sentences to logical form structured classification with probabilistic categorial grammars":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "break it down a question understanding benchmark")
  Member "a joint model of language and perception for grounded attribute learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "cops ref a new dataset and task on compositional referring expression comprehension")
  Member "learning dependency based compositional semantics":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "break it down a question understanding benchmark")
  Member "acquiring word meaning mappings for natural language interfaces":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "exploring neural models for parsing natural language into first order logic")
  Member "the symbol grounding problem":
    also connected through other hyperedges:
      - Hyperedge HE-6 (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.CL(Computation and Language)