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 "mapping instructions to actions in 3d environments with visual goal prediction"):
  Abstract: We propose to decompose instruction execution to goal prediction and action generation. We design a model that maps raw visual observations to goals using LINGUNET, a language-conditioned image generation network, and then generates the actions required to complete them. Our model is trained from demonstration only without external resources. To evaluate our approach, we introduce two benchmarks for instruction following: LANI, a navigation task; and CHAI, where an agent executes household instructions. Our evaluation demonstrates the advantages of our model decomposition, and illustrates the challenges posed by our new benchmarks.

Includes members:
  Member "scheduled policy optimization for natural language communication with intelligent agents":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "look before you leap bridging model free and model based reinforcement learning for planned ahead vision and language navigation")
  Member "chalet cornell house agent learning environment":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning to follow directions in street view")
  Member "speaker follower models for vision and language navigation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "from seeing to moving a survey on learning for visual indoor navigation vin")
  Member "situated mapping of sequential instructions to actions with single step reward observation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "editing based sql query generation for cross domain context dependent questions")
  Member "representation learning for grounded spatial reasoning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "following instructions by imagining and reaching visual goals")
  Member "zero shot task generalization with multi task deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on catastrophic interference in atari 2600 games")
  Member "mapping instructions and visual observations to actions with reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "towards learning a generic agent for vision and language navigation via pre training")
  Member "grounded language learning in a simulated 3d world":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "exploiting language instructions for interpretable and compositional reinforcement learning")

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)