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 "the regretful agent heuristic aided navigation through progress estimation"):
  Abstract: As deep learning continues to make progress for challenging perception tasks, there is increased interest in combining vision, language, and decision-making. Specifically, the Vision and Language Navigation (VLN) task involves navigating to a goal purely from language instructions and visual information without explicit knowledge of the goal. Recent successful approaches have made in-roads in achieving good success rates for this task but rely on beam search, which thoroughly explores a large number of trajectories and is unrealistic for applications such as robotics. In this paper, inspired by the intuition of viewing the problem as search on a navigation graph, we propose to use a progress monitor developed in prior work as a learnable heuristic for search. We then propose two modules incorporated into an end-to-end architecture: 1) A learned mechanism to perform backtracking, which decides whether to continue moving forward or roll back to a previous state (Regret Module) and 2) A mechanism to help the agent decide which direction to go next by showing directions that are visited and their associated progress estimate (Progress Marker). Combined, the proposed approach significantly outperforms current state-of-the-art methods using greedy action selection, with 5% absolute improvement on the test server in success rates, and more importantly 8% on success rates normalized by the path length. Our code is available at this https URL .

Includes members:
  Member "self monitoring navigation agent via auxiliary progress estimation":
    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 "reinforced cross modal matching and self supervised imitation learning for vision language navigation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "from seeing to moving a survey on learning for visual indoor navigation vin")
  Member "talk the walk navigating new york city through grounded dialogue":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "mvp unified motion and visual self supervised learning for large scale robotic navigation")
  Member "gibson env real world perception for embodied agents":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "an exploration of embodied visual exploration")
  Member "speaker follower models for vision and language navigation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "from seeing to moving a survey on learning for visual indoor navigation vin")
  Member "guided feature transformation gft a neural language grounding module for embodied agents":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "self monitoring navigation agent via auxiliary progress estimation")
  Member "on evaluation of embodied navigation agents":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "an exploration of embodied visual exploration")
  Member "building generalizable agents with a realistic and rich 3d environment":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "citylearn diverse real world environments for sample efficient navigation policy 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.AI(Artificial Intelligence)