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: "geometry aware recurrent neural networks for active visual recognition"
  Abstract: We present recurrent geometry-aware neural networks that integrate visual information across multiple views of a scene into 3D latent feature tensors, while maintaining an one-to-one mapping between 3D physical locations in the world scene and latent feature locations. Object detection, object segmentation, and 3D reconstruction is then carried out directly using the constructed 3D feature memory, as opposed to any of the input 2D images. The proposed models are equipped with differentiable egomotion-aware feature warping and (learned) depth-aware unprojection operations to achieve geometrically consistent mapping between the features in the input frame and the constructed latent model of the scene. We empirically show the proposed model generalizes much better than geometryunaware LSTM/GRU networks, especially under the presence of multiple objects and cross-object occlusions. Combined with active view selection policies, our model learns to select informative viewpoints to integrate information from by "undoing" cross-object occlusions, seamlessly combining geometry with learning from experience.

Connected through hyperedge HE-1 (induced by source paper "deep reinforcement learning for active human pose estimation"):
  with members:
    - "ordinal depth supervision for 3d human pose estimation"
    - "total capture a 3d deformation model for tracking faces hands and bodies"
    - "gibson env real world perception for embodied agents"
    - "reinforcement learning of active vision for manipulating objects under occlusions"
    - "learning cooperative visual dialog agents with deep reinforcement learning"
    - "end to end recovery of human shape and pose"
    - "a dataset for developing and benchmarking active vision"
    - "towards accurate multi person pose estimation in the wild"

Connected through hyperedge HE-2 (induced by source paper "active 6d multi object pose estimation in cluttered scenarios with deep reinforcement learning"):
  with members:
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "instance and category level 6d object pose estimation"
    - "normalized object coordinate space for category level 6d object pose and size estimation"
    - "task oriented hand motion retargeting for dexterous manipulation imitation"
    - "bop benchmark for 6d object pose estimation"
    - "multi task deep networks for depth based 6d object pose and joint registration in crowd scenarios"
    - "pvnet pixel wise voting network for 6dof pose estimation"
    - "deep object centric policies for autonomous driving"

Connected through hyperedge HE-3 (induced by source paper "groundnet monocular ground plane normal estimation with geometric consistency"):
  with members:
    - "a baseline for 3d multi object tracking"
    - "real time correlation tracking via joint model compression and transfer"
    - "monocular 3d object detection with pseudo lidar point cloud"
    - "learning correspondence from the cycle consistency of time"
    - "unsupervised deep tracking"
    - "future near collision prediction from monocular video feasibility dataset and challenges"
    - "layoutnet reconstructing the 3d room layout from a single rgb image"
    - "deep reinforcement learning for autonomous driving"

Connected through hyperedge HE-4 (induced by source paper "groundnet segmentation aware monocular ground plane estimation with geometric consistency"):
  with members:
    - "a baseline for 3d multi object tracking"
    - "monocular 3d object detection with pseudo lidar point cloud"
    - "learning correspondence from the cycle consistency of time"
    - "unsupervised deep tracking"
    - "future near collision prediction from monocular video feasibility dataset and challenges"
    - "layoutnet reconstructing the 3d room layout from a single rgb image"
    - "deep reinforcement learning for autonomous driving"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"

Connected through hyperedge HE-5 (induced by source paper "learning spatial common sense with geometry aware recurrent networks"):
  with members:
    - "fusion volumetric object level slam"
    - "voxelnet end to end learning for point cloud based 3d object detection"
    - "learning a multi view stereo machine"
    - "unsupervised learning of depth and ego motion from video"
    - "neural map structured memory for deep reinforcement learning"
    - "multi view supervision for single view reconstruction via differentiable ray consistency"
    - "marrnet 3d shape reconstruction via 2 5d sketches"
    - "learning 3d object categories by looking around them"

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)