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 with dual attention and spatial memory"):
  Abstract: The role of robots in society keeps expanding, bringing with it the necessity of interacting and communicating with humans. In order to keep such interaction intuitive, we provide automatic wayfinding based on verbal navigational instructions. 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 dual 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 spatial 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 project page: https://www.trace.ethz.ch/publications/2019/talk2nav/index.html

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 "learning accurate comfortable and human like driving":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "real time 3d traffic cone detection for autonomous driving")
  Member "learning to follow directions in street view":
    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 "grounding human to vehicle advice for self driving vehicles":
    also connected through other hyperedges:
      - Hyperedge HE-4 (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-5 (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-6 (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-7 (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-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)