human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "scheduled policy optimization for natural language communication with intelligent agents"
  Abstract: We investigate the task of learning to follow natural language instructions by jointly reasoning with visual observations and language inputs. In contrast to existing methods which start with learning from demonstrations (LfD) and then use reinforcement learning (RL) to fine-tune the model parameters, we propose a novel policy optimization algorithm which dynamically schedules demonstration learning and RL. The proposed training paradigm provides efficient exploration and better generalization beyond existing methods. Comparing to existing ensemble models, the best single model based on our proposed method tremendously decreases the execution error by over 50% on a block-world environment. To further illustrate the exploration strategy of our RL algorithm, We also include systematic studies on the evolution of policy entropy during training.

Connected through 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"):
  with members:
    - "watch listen and describe globally and locally aligned cross modal attentions for video captioning"
    - "no metrics are perfect adversarial reward learning for visual storytelling"
    - "video captioning via hierarchical reinforcement learning"
    - "value prediction network"
    - "matterport3d learning from rgb d data in indoor environments"
    - "curiosity driven exploration by self supervised prediction"
    - "mapping instructions and visual observations to actions with reinforcement learning"
    - "imagination augmented agents for deep reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "mapping instructions to actions in 3d environments with visual goal prediction"):
  with members:
    - "chalet cornell house agent learning environment"
    - "speaker follower models for vision and language navigation"
    - "situated mapping of sequential instructions to actions with single step reward observation"
    - "representation learning for grounded spatial reasoning"
    - "zero shot task generalization with multi task deep reinforcement learning"
    - "mapping instructions and visual observations to actions with reinforcement learning"
    - "grounded language learning in a simulated 3d world"
    - "iqa visual question answering in interactive environments"

Question: Please tell me which class the center node 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)