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: "learning to follow directions in street view"
  Abstract: Navigating and understanding the real world remains a key challenge in machine learning and inspires a great variety of research in areas such as language grounding, planning, navigation and computer vision. We propose an instruction-following task that requires all of the above, and which combines the practicality of simulated environments with the challenges of ambiguous, noisy real world data. StreetNav is built on top of Google Street View and provides visually accurate environments representing real places. Agents are given driving instructions which they must learn to interpret in order to successfully navigate in this environment. Since humans equipped with driving instructions can readily navigate in previously unseen cities, we set a high bar and test our trained agents for similar cognitive capabilities. Although deep reinforcement learning (RL) methods are frequently evaluated only on data that closely follow the training distribution, our dataset extends to multiple cities and has a clean train/test separation. This allows for thorough testing of generalisation ability. This paper presents the StreetNav environment and tasks, a set of novel models that establish strong baselines, and analysis of the task and the trained agents.

Connected through hyperedge HE-1 (induced by source paper "mvp unified motion and visual self supervised learning for large scale robotic navigation"):
  with members:
    - "badgr an autonomous self supervised learning based navigation system"
    - "kidnapped radar topological radar localisation using rotationally invariant metric learning"
    - "a hybrid compact neural architecture for visual place recognition"
    - "levelling the playing field a comprehensive comparison of visual place recognition approaches under changing conditions"
    - "beyond photometric loss for self supervised ego motion estimation"
    - "towards navigation without precise localization weakly supervised learning of goal directed navigation cost map"
    - "the oxford radar robotcar dataset a radar extension to the oxford robotcar dataset"
    - "self supervised learning of depth and motion under photometric inconsistency"

Connected through hyperedge HE-2 (induced by source paper "citylearn diverse real world environments for sample efficient navigation policy learning"):
  with members:
    - "a hybrid compact neural architecture for visual place recognition"
    - "levelling the playing field a comprehensive comparison of visual place recognition approaches under changing conditions"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "visual place recognition for aerial robotics exploring accuracy computation trade off for local image descriptors"
    - "cross view policy learning for street navigation"
    - "automatic coverage selection for surface based visual localization"
    - "the streetlearn environment and dataset"
    - "multi process fusion visual place recognition using multiple image processing methods"

Connected through hyperedge HE-3 (induced by source paper "visual grounding in video for unsupervised word translation"):
  with members:
    - "end to end learning of visual representations from uncurated instructional videos"
    - "howto100m learning a text video embedding by watching hundred million narrated video clips"
    - "unsupervised multi modal neural machine translation"
    - "learning visual question answering by bootstrapping hard attention"
    - "lifelong learning for image captioning by asking natural language questions"
    - "on the limitations of unsupervised bilingual dictionary induction"
    - "a robust self learning method for fully unsupervised cross lingual mappings of word embeddings"
    - "quo vadis action recognition a new model and the kinetics dataset"

Connected through hyperedge HE-4 (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"
    - "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"
    - "touchdown natural language navigation and spatial reasoning in visual street environments"

Connected through hyperedge HE-5 (induced by source paper "transferable representation learning in vision and language navigation"):
  with members:
    - "multi modal discriminative model for vision and language navigation"
    - "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"
    - "end to end retrieval in continuous space"
    - "talk the walk navigating new york city through grounded dialogue"
    - "speaker follower models for vision and language navigation"

Connected through hyperedge HE-6 (induced by source paper "self educated language agent with hindsight experience replay for instruction following"):
  with members:
    - "language as an abstraction for hierarchical deep reinforcement learning"
    - "from language to goals inverse reinforcement learning for vision based instruction following"
    - "visual hindsight experience replay"
    - "actrce augmenting experience via teacher s advice for multi goal reinforcement learning"
    - "competitive experience replay"
    - "a survey of reinforcement learning informed by natural language"
    - "babyai first steps towards grounded language learning with a human in the loop"
    - "reinforced cross modal matching and self supervised imitation learning for vision language navigation"

Connected through hyperedge HE-7 (induced by source paper "cross view policy learning for street navigation"):
  with members:
    - "the streetlearn environment and dataset"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "reinforced cross modal matching and self supervised imitation learning for vision language navigation"
    - "global pose estimation with an attention based recurrent network"
    - "touchdown natural language navigation and spatial reasoning in visual street environments"
    - "the adobeindoornav dataset towards deep reinforcement learning based real world indoor robot visual navigation"
    - "unsupervised predictive memory in a goal directed agent"
    - "modality distillation with multiple stream networks for action recognition"

Connected through hyperedge HE-8 (induced by source paper "navigation agents for the visually impaired a sidewalk simulator and experiments"):
  with members:
    - "the apolloscape open dataset for autonomous driving and its application"
    - "pyramid mask text detector"
    - "the streetlearn environment and dataset"
    - "multimodal interaction aware motion prediction for autonomous street crossing"
    - "talk the walk navigating new york city through grounded dialogue"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "gibson env real world perception for embodied agents"
    - "speaker follower models for vision and language navigation"

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.AI(Artificial Intelligence)