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 "learning view and target invariant visual servoing for navigation"):
  Abstract: The advances in deep reinforcement learning recently revived interest in data-driven learning based approaches to navigation. In this paper we propose to learn viewpoint invariant and target invariant visual servoing for local mobile robot navigation; given an initial view and the goal view or an image of a target, we train deep convolutional network controller to reach the desired goal. We present a new architecture for this task which rests on the ability of establishing correspondences between the initial and goal view and novel reward structure motivated by the traditional feedback control error. The advantage of the proposed model is that it does not require calibration and depth information and achieves robust visual servoing in a variety of environments and targets without any parameter fine tuning. We present comprehensive evaluation of the approach and comparison with other deep learning architectures as well as classical visual servoing methods in visually realistic simulation environment. The presented model overcomes the brittleness of classical visual servoing based methods and achieves significantly higher generalization capability compared to the previous learning approaches.

Includes members:
  Member "neural autonomous navigation with riemannian motion policy":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "badgr an autonomous self supervised learning based navigation system")
  Member "divis domain invariant visual servoing for collision free goal reaching":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "splitnet sim2sim and task2task transfer for embodied visual navigation")
  Member "combining optimal control and learning for visual navigation in novel environments":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "citylearn diverse real world environments for sample efficient navigation policy learning")
  Member "visual memory for robust path following":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "reactive navigation in partially familiar planar environments using semantic perceptual feedback")
  Member "zero shot visual imitation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "universal value density estimation for imitation learning and goal conditioned reinforcement learning")
  Member "gaple generalizable approaching policy learning for robotic object searching in indoor environment":
    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 "gibson env real world perception for embodied agents":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "an exploration of embodied visual exploration")
  Member "semi parametric topological memory for navigation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "ego topo environment affordances from egocentric video")

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)