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 "talk2nav long range vision and language navigation in cities"):
  Abstract: Autonomous driving models often consider the goal as fixed at the start of the ride. Yet, in practice, passengers will still want to influence the route, e.g. to pick up something along the way. In order to keep such inputs intuitive, we provide automatic way finding in cities based on verbal navigational instructions and street-view images. Our first contribution is the creation of a large-scale dataset with verbal navigation instructions. To this end, we have developed an interactive visual navigation environment based on Google Street View; we further design an annotation method to highlight mined anchor landmarks and local directions between them in order to help annotators formulate typical, human references to those. The annotation task was crowdsourced on the AMT platform, to construct a new Talk2Nav dataset with 10,714 routes. Our second contribution is a new learning method. Inspired by spatial cognition research on the mental conceptualization of navigational instructions, we introduce a soft attention mechanism defined over the segmented language instructions to jointly extract two partial instructions -- one for matching the next upcoming visual landmark and the other for matching the local directions to the next landmark. On the similar lines, we also introduce memory scheme to encode the local directional transitions. Our work takes advantage of the advance in two lines of research: mental formalization of verbal navigational instructions and training neural network agents for automatic way finding. Extensive experiments show that our method significantly outperforms previous navigation methods. For demo video, dataset and code, please refer to our \href{this https URL}{project page}.

Includes members:
  Member "tactical rewind self correction via backtracking in vision and language navigation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "environment agnostic multitask learning for natural language grounded navigation")
  Member "language conditioned graph networks for relational reasoning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "unshuffling data for improved generalization")
  Member "grounding human to vehicle advice for self driving vehicles":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "who make drivers stop towards driver centric risk assessment risk object identification via causal inference")
  Member "self monitoring navigation agent via auxiliary progress estimation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "from seeing to moving a survey on learning for visual indoor navigation vin")
  Member "the regretful agent heuristic aided navigation through progress estimation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "environment agnostic multitask learning for natural language grounded navigation")
  Member "reinforced cross modal matching and self supervised imitation learning for vision language navigation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (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-7 (induced by source paper "mvp unified motion and visual self supervised learning for large scale robotic navigation")
  Member "touchdown natural language navigation and spatial reasoning in visual street environments":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "mvp unified motion and visual self supervised learning for large scale robotic navigation")

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)