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 "look before you leap bridging model free and model based reinforcement learning for planned ahead vision and language navigation"):
  Abstract: Existing research studies on vision and language grounding for robot navigation focus on improving model-free deep reinforcement learning (DRL) models in synthetic environments. However, model-free DRL models do not consider the dynamics in the real-world environments, and they often fail to generalize to new scenes. In this paper, we take a radical approach to bridge the gap between synthetic studies and real-world practices---We propose a novel, planned-ahead hybrid reinforcement learning model that combines model-free and model-based reinforcement learning to solve a real-world vision-language navigation task. Our look-ahead module tightly integrates a look-ahead policy model with an environment model that predicts the next state and the reward. Experimental results suggest that our proposed method significantly outperforms the baselines and achieves the best on the real-world Room-to-Room dataset. Moreover, our scalable method is more generalizable when transferring to unseen environments, and the relative success rate is increased by 15.5% on the unseen test set.

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 "mapping instructions to actions in 3d environments with visual goal prediction")
  Member "watch listen and describe globally and locally aligned cross modal attentions for video captioning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multi modal dense video captioning")
  Member "no metrics are perfect adversarial reward learning for visual storytelling":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "paraphrase generation with latent bag of words")
  Member "video captioning via hierarchical reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multi modal dense video captioning")
  Member "value prediction network":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "plannable approximations to mdp homomorphisms equivariance under actions")
  Member "matterport3d learning from rgb d data in indoor environments":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "an exploration of embodied visual exploration")
  Member "curiosity driven exploration by self supervised prediction":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deep reinforcement learning for autonomous driving a survey")
  Member "mapping instructions and visual observations to actions with reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "towards learning a generic agent for vision and language navigation via pre training")

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.CV(Computer Vision and Pattern Recognition)