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 spatial common sense with geometry aware recurrent networks"
  Abstract: We integrate two powerful ideas, geometry and deep visual representation learning, into recurrent network architectures for mobile visual scene understanding. The proposed networks learn to "lift" and integrate 2D visual features over time into latent 3D feature maps of the scene. They are equipped with differentiable geometric operations, such as projection, unprojection, egomotion estimation and stabilization, in order to compute a geometrically-consistent mapping between the world scene and their 3D latent feature state. We train the proposed architectures to predict novel camera views given short frame sequences as input. Their predictions strongly generalize to scenes with a novel number of objects, appearances and configurations; they greatly outperform previous works that do not consider egomotion stabilization or a space-aware latent feature state. We train the proposed architectures to detect and segment objects in 3D using the latent 3D feature map as input--as opposed to per frame features. The resulting object detections persist over time: they continue to exist even when an object gets occluded or leaves the field of view. Our experiments suggest the proposed space-aware latent feature memory and egomotion-stabilized convolutions are essential architectural choices for spatial common sense to emerge in artificial embodied visual agents.

Connected through hyperedge HE-1 (induced by source paper "learning from unlabelled videos using contrastive predictive neural 3d mapping"):
  with members:
    - "nuscenes a multimodal dataset for autonomous driving"
    - "pifu pixel aligned implicit function for high resolution clothed human digitization"
    - "deepvoxels learning persistent 3d feature embeddings"
    - "sequential attend infer repeat generative modelling of moving objects"
    - "learning to decompose and disentangle representations for video prediction"
    - "representation learning with contrastive predictive coding"
    - "learning a multi view stereo machine"
    - "carla an open urban driving simulator"

Connected through hyperedge HE-2 (induced by source paper "a neural rendering framework for free viewpoint relighting"):
  with members:
    - "deferred neural rendering image synthesis using neural textures"
    - "visual representation learning with 3d view contrastive inverse graphics networks"
    - "transformable bottleneck networks"
    - "differentiable surface splatting for point based geometry processing"
    - "pix2vex image to geometry reconstruction using a smooth differentiable renderer"
    - "monet unsupervised scene decomposition and representation"
    - "scene representation networks continuous 3d structure aware neural scene representations"
    - "neural point based graphics"

Connected through hyperedge HE-3 (induced by source paper "visual representation learning with 3d view contrastive inverse graphics networks"):
  with members:
    - "nuscenes a multimodal dataset for autonomous driving"
    - "deepvoxels learning persistent 3d feature embeddings"
    - "learning to decompose and disentangle representations for video prediction"
    - "tracking emerges by colorizing videos"
    - "representation learning with contrastive predictive coding"
    - "sampling matters in deep embedding learning"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "unsupervised representation learning by sorting sequences"

Connected through hyperedge HE-4 (induced by source paper "learnable triangulation of human pose"):
  with members:
    - "self supervised learning of 3d human pose using multi view geometry"
    - "rethinking pose in 3d multi stage refinement and recovery for markerless motion capture"
    - "a generalizable approach for multi view 3d human pose regression"
    - "humbi 1 0 human multiview behavioral imaging dataset"
    - "3d human pose estimation in video with temporal convolutions and semi supervised training"
    - "simple baselines for human pose estimation and tracking"
    - "monocular total capture posing face body and hands in the wild"
    - "exploiting temporal information for 3d human pose estimation"

Connected through hyperedge HE-5 (induced by source paper "embodied language grounding with implicit 3d visual feature representations"):
  with members:
    - "the neuro symbolic concept learner interpreting scenes words and sentences from natural supervision"
    - "cycle consistency for robust visual question answering"
    - "deepvoxels learning persistent 3d feature embeddings"
    - "learning a multi view stereo machine"
    - "wikireading a novel large scale language understanding task over wikipedia"
    - "text understanding with the attention sum reader network"
    - "learning to compose neural networks for question answering"
    - "gated attention readers for text comprehension"

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)