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: "the streetlearn environment and dataset"
  Abstract: Navigation is a rich and well-grounded problem domain that drives progress in many different areas of research: perception, planning, memory, exploration, and optimisation in particular. Historically these challenges have been separately considered and solutions built that rely on stationary datasets - for example, recorded trajectories through an environment. These datasets cannot be used for decision-making and reinforcement learning, however, and in general the perspective of navigation as an interactive learning task, where the actions and behaviours of a learning agent are learned simultaneously with the perception and planning, is relatively unsupported. Thus, existing navigation benchmarks generally rely on static datasets (Geiger et al., 2013; Kendall et al., 2015) or simulators (Beattie et al., 2016; Shah et al., 2018). To support and validate research in end-to-end navigation, we present StreetLearn: an interactive, first-person, partially-observed visual environment that uses Google Street View for its photographic content and broad coverage, and give performance baselines for a challenging goal-driven navigation task. The environment code, baseline agent code, and the dataset are available at this http URL

Connected through hyperedge HE-1 (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"
    - "learning to follow directions in street view"
    - "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"
    - "multi process fusion visual place recognition using multiple image processing methods"

Connected through hyperedge HE-2 (induced by source paper "retouchdown adding touchdown to streetlearn as a shareable resource for language grounding tasks in street view"):
  with members:
    - "navigation agents for the visually impaired a sidewalk simulator and experiments"
    - "vision language navigation with self supervised auxiliary reasoning tasks"
    - "valan vision and language agent navigation"
    - "effective and general evaluation for instruction conditioned navigation using dynamic time warping"
    - "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"
    - "mapping instructions to actions in 3d environments with visual goal prediction"

Connected through hyperedge HE-3 (induced by source paper "driving datasets literature review"):
  with members:
    - "eurocity persons a novel benchmark for person detection in traffic scenes"
    - "blvd building a large scale 5d semantics benchmark for autonomous driving"
    - "the apolloscape open dataset for autonomous driving and its application"
    - "the h3d dataset for full surround 3d multi object detection and tracking in crowded urban scenes"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "object detection in 20 years a survey"
    - "deep multi modal object detection and semantic segmentation for autonomous driving datasets methods and challenges"
    - "on the importance of stereo for accurate depth estimation an efficient semi supervised deep neural network approach"

Connected through hyperedge HE-4 (induced by source paper "cross view policy learning for street navigation"):
  with members:
    - "learning to follow directions in street view"
    - "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-5 (induced by source paper "navigation agents for the visually impaired a sidewalk simulator and experiments"):
  with members:
    - "learning to follow directions in street view"
    - "the apolloscape open dataset for autonomous driving and its application"
    - "pyramid mask text detector"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "effective and general evaluation for instruction conditioned navigation using dynamic time warping"):
  with members:
    - "from language to goals inverse reinforcement learning for vision based instruction following"
    - "stay on the path instruction fidelity in vision and language navigation"
    - "touchdown natural language navigation and spatial reasoning in visual street environments"
    - "on evaluation of embodied navigation agents"
    - "mapping instructions to actions in 3d environments with visual goal prediction"
    - "mapping navigation instructions to continuous control actions with position visitation prediction"
    - "matterport3d learning from rgb d data in indoor environments"
    - "vision and language navigation interpreting visually grounded navigation instructions in real environments"

Connected through hyperedge HE-7 (induced by source paper "general evaluation for instruction conditioned navigation using dynamic time warping"):
  with members:
    - "rerere remote embodied referring expressions in real indoor environments"
    - "stay on the path instruction fidelity in vision and language navigation"
    - "touchdown natural language navigation and spatial reasoning in visual street environments"
    - "on evaluation of embodied navigation agents"
    - "mapping instructions to actions in 3d environments with visual goal prediction"
    - "mapping navigation instructions to continuous control actions with position visitation prediction"
    - "vision and language navigation interpreting visually grounded navigation instructions in real environments"
    - "voice recognition algorithms using mel frequency cepstral coefficient mfcc and dynamic time warping dtw techniques"

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)