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 "improving grounded natural language understanding through human robot dialog"):
  Abstract: Natural language understanding for robotics can require substantial domain- and platform-specific engineering. For example, for mobile robots to pick-and-place objects in an environment to satisfy human commands, we can specify the language humans use to issue such commands, and connect concept words like red can to physical object properties. One way to alleviate this engineering for a new domain is to enable robots in human environments to adapt dynamically---continually learning new language constructions and perceptual concepts. In this work, we present an end-to-end pipeline for translating natural language commands to discrete robot actions, and use clarification dialogs to jointly improve language parsing and concept grounding. We train and evaluate this agent in a virtual setting on Amazon Mechanical Turk, and we transfer the learned agent to a physical robot platform to demonstrate it in the real world.

Includes members:
  Member "teaching robots to imitate a human with no on teacher sensors what are the key challenges":
    also connected through other hyperedges:
      (none)
  Member "look before you leap bridging model free and model based reinforcement learning for planned ahead vision and language navigation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "from seeing to moving a survey on learning for visual indoor navigation vin")
  Member "follownet robot navigation by following natural language directions with deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep compositional robotic planners that follow natural language commands")
  Member "visual curiosity learning to ask questions to learn visual recognition":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "challenges and prospects in vision and language research")
  Member "neural amr sequence to sequence models for parsing and generation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "modeling global and local node contexts for text generation from knowledge graphs")
  Member "learning interpretable spatial operations in a rich 3d blocks world":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "why build an assistant in minecraft")
  Member "vision and language navigation interpreting visually grounded navigation instructions in real environments":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "towards efficient model compression via learned global ranking")
  Member "language to logical form with neural attention":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "unsupervised question decomposition for 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)