human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "speaker follower models for vision and language navigation"
  Abstract: Navigation guided by natural language instructions presents a challenging reasoning problem for instruction followers. Natural language instructions typically identify only a few high-level decisions and landmarks rather than complete low-level motor behaviors; much of the missing information must be inferred based on perceptual context. In machine learning settings, this is doubly challenging: it is difficult to collect enough annotated data to enable learning of this reasoning process from scratch, and also difficult to implement the reasoning process using generic sequence models. Here we describe an approach to vision-and-language navigation that addresses both these issues with an embedded speaker model. We use this speaker model to (1) synthesize new instructions for data augmentation and to (2) implement pragmatic reasoning, which evaluates how well candidate action sequences explain an instruction. Both steps are supported by a panoramic action space that reflects the granularity of human-generated instructions. Experiments show that all three components of this approach---speaker-driven data augmentation, pragmatic reasoning and panoramic action space---dramatically improve the performance of a baseline instruction follower, more than doubling the success rate over the best existing approach on a standard benchmark.

Connected through hyperedge HE-1 (induced by source paper "from seeing to moving a survey on learning for visual indoor navigation vin"):
  with members:
    - "walking with mind mental imagery enhanced embodied qa"
    - "what should i do now marrying reinforcement learning and symbolic planning"
    - "are we making real progress in simulated environments measuring the sim2real gap in embodied visual navigation"
    - "embodied question answering in photorealistic environments with point cloud perception"
    - "graph attention memory for visual navigation"
    - "vision language navigation with self supervised auxiliary reasoning tasks"
    - "multi target embodied question answering"
    - "learning your way without map or compass panoramic target driven visual navigation"

Connected through hyperedge HE-2 (induced by source paper "environment agnostic multitask learning for natural language grounded navigation"):
  with members:
    - "multi modal discriminative model for vision and language navigation"
    - "vision and dialog navigation"
    - "tactical rewind self correction via backtracking in vision and language navigation"
    - "domain agnostic learning with disentangled representations"
    - "valan vision and language agent navigation"
    - "stay on the path instruction fidelity in vision and language navigation"
    - "transferable representation learning in vision and language navigation"
    - "the regretful agent heuristic aided navigation through progress estimation"

Connected through hyperedge HE-3 (induced by source paper "towards learning a generic agent for vision and language navigation via pre training"):
  with members:
    - "vision and dialog navigation"
    - "tactical rewind self correction via backtracking in vision and language navigation"
    - "lxmert learning cross modality encoder representations from transformers"
    - "unicoder vl a universal encoder for vision and language by cross modal pre training"
    - "vilbert pretraining task agnostic visiolinguistic representations for vision and language tasks"
    - "robust navigation with language pretraining and stochastic sampling"
    - "videobert a joint model for video and language representation learning"
    - "vl bert pre training of generic visual linguistic representations"

Connected through hyperedge HE-4 (induced by source paper "vision dialog navigation by exploring cross modal memory"):
  with members:
    - "reasoning visual dialogs with structural and partial observations"
    - "dual attention networks for visual reference resolution in visual dialog"
    - "vision language navigation with self supervised auxiliary reasoning tasks"
    - "multi step reasoning via recurrent dual attention for visual dialog"
    - "self monitoring navigation agent via auxiliary progress estimation"
    - "factor graph attention"
    - "learning to navigate unseen environments back translation with environmental dropout"
    - "reinforced cross modal matching and self supervised imitation learning for vision language navigation"

Connected through hyperedge HE-5 (induced by source paper "multi view learning for vision and language navigation"):
  with members:
    - "towards learning a generic agent for vision and language navigation via pre training"
    - "tactical rewind self correction via backtracking in vision and language navigation"
    - "vision language navigation with self supervised auxiliary reasoning tasks"
    - "self monitoring navigation agent via auxiliary progress estimation"
    - "the regretful agent heuristic aided navigation through progress estimation"
    - "learning to navigate unseen environments back translation with environmental dropout"
    - "reinforced cross modal matching and self supervised imitation learning for vision language navigation"
    - "touchdown natural language navigation and spatial reasoning in visual street environments"

Connected through hyperedge HE-6 (induced by source paper "reverie remote embodied visual referring expression in real indoor environments"):
  with members:
    - "improving referring expression grounding with cross modal attention guided erasing"
    - "tactical rewind self correction via backtracking in vision and language navigation"
    - "the regretful agent heuristic aided navigation through progress estimation"
    - "reinforced cross modal matching and self supervised imitation learning for vision language navigation"
    - "talk the walk navigating new york city through grounded dialogue"
    - "touchdown natural language navigation and spatial reasoning in visual street environments"
    - "vision based navigation with language based assistance via imitation learning with indirect intervention"
    - "mattnet modular attention network for referring expression comprehension"

Connected through hyperedge HE-7 (induced by source paper "learning to follow directions in street view"):
  with members:
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "learning visual question answering by bootstrapping hard attention"
    - "global pose estimation with an attention based recurrent network"
    - "talk the walk navigating new york city through grounded dialogue"
    - "touchdown natural language navigation and spatial reasoning in visual street environments"
    - "gibson env real world perception for embodied agents"
    - "chalet cornell house agent learning environment"

Connected through hyperedge HE-8 (induced by source paper "talk2nav long range vision and language navigation with dual attention and spatial memory"):
  with members:
    - "tactical rewind self correction via backtracking in vision and language navigation"
    - "learning accurate comfortable and human like driving"
    - "learning to follow directions in street view"
    - "grounding human to vehicle advice for self driving vehicles"
    - "self monitoring navigation agent via auxiliary progress estimation"
    - "the regretful agent heuristic aided navigation through progress estimation"
    - "reinforced cross modal matching and self supervised imitation learning for vision language navigation"
    - "talk the walk navigating new york city through grounded dialogue"

Question: Please tell me which class the center node 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)