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: "rgbd datasets past present and future"
  Abstract: Since the launch of the Microsoft Kinect, scores of RGBD datasets have been released. These have propelled advances in areas from reconstruction to gesture recognition. In this paper we explore the field, reviewing datasets across eight categories: semantics, object pose estimation, camera tracking, scene reconstruction, object tracking, human actions, faces and identification. By extracting relevant information in each category we help researchers to find appropriate data for their needs, and we consider which datasets have succeeded in driving computer vision forward and why. #R##N#Finally, we examine the future of RGBD datasets. We identify key areas which are currently underexplored, and suggest that future directions may include synthetic data and dense reconstructions of static and dynamic scenes.

Connected through hyperedge HE-1 (induced by source paper "extending maps with semantic and contextual object information for robot navigation a learning based framework using visual and depth cues"):
  with members:
    - "kaolin a pytorch library for accelerating 3d deep learning research"
    - "yolact real time instance segmentation"
    - "mapping road lanes using laser remission and deep neural networks"
    - "end to end recovery of human shape and pose"
    - "mix and match tuning for self supervised semantic segmentation"
    - "tracking the untrackable learning to track multiple cues with long term dependencies"
    - "pedestrian prediction by planning using deep neural networks"
    - "mask r cnn"

Connected through hyperedge HE-2 (induced by source paper "low viewpoint forest depth dataset for sparse rover swarms"):
  with members:
    - "monocular depth estimation a survey"
    - "structured attention guided convolutional neural fields for monocular depth estimation"
    - "high quality monocular depth estimation via transfer learning"
    - "deeper depth prediction with fully convolutional residual networks"
    - "depth map prediction from a single image using a multi scale deep network"

Connected through hyperedge HE-3 (induced by source paper "dfki cabin simulator a test platform for visual in cabin monitoring functions"):
  with members:
    - "sviro synthetic vehicle interior rear seat occupancy dataset and benchmark"
    - "in the wild human pose estimation using explicit 2d features and intermediate 3d representations"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-4 (induced by source paper "learning with privileged information via adversarial discriminative modality distillation"):
  with members:
    - "fast converging conditional generative adversarial networks for image synthesis"
    - "modality distillation with multiple stream networks for action recognition"
    - "adversarial network compression"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "non local neural networks"
    - "learning and refining of privileged information based rnns for action recognition from depth sequences"
    - "interpretable 3d human action analysis with temporal convolutional networks"

Connected through hyperedge HE-5 (induced by source paper "bonn activity maps dataset description"):
  with members:
    - "efficient 3d reconstruction and streaming for group scale multi client live telepresence"
    - "a dataset of multi illumination images in the wild"
    - "the replica dataset a digital replica of indoor spaces"
    - "ntu rgb d 120 a large scale benchmark for 3d human activity understanding"
    - "latticenet fast point cloud segmentation using permutohedral lattices"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "interiornet mega scale multi sensor photo realistic indoor scenes dataset"
    - "joint 2d 3d semantic data for indoor scene understanding"

Connected through hyperedge HE-6 (induced by source paper "learning coupled spatial temporal attention for skeleton based action recognition"):
  with members:
    - "action recognition with spatio temporal visual attention on skeleton image sequences"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "co occurrence feature learning from skeleton data for action recognition and detection with hierarchical aggregation"
    - "modeling temporal dynamics and spatial configurations of actions using two stream recurrent neural networks"
    - "skeleton based action recognition using lstm and cnn"
    - "skeleton based action recognition using spatio temporal lstm network with trust gates"
    - "a new representation of skeleton sequences for 3d action recognition"
    - "view adaptive recurrent neural networks for high performance human action recognition from skeleton data"

Connected through hyperedge HE-7 (induced by source paper "large scale 6d object pose estimation dataset for industrial bin picking"):
  with members:
    - "bop benchmark for 6d object pose estimation"
    - "defining the pose of any 3d rigid object and an associated distance"
    - "learning dexterous in hand manipulation"
    - "t less an rgb d dataset for 6d pose estimation of texture less objects"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "posecnn a convolutional neural network for 6d object pose estimation in cluttered scenes"
    - "symmetry aware evaluation of 3d object detection and pose estimation in scenes of many parts in bulk"
    - "the cityscapes dataset for semantic urban scene understanding"

Connected through hyperedge HE-8 (induced by source paper "gans nqm a generative adversarial networks based no reference quality assessment metric for rgb d synthesized views"):
  with members:
    - "dense 3d object reconstruction from a single depth view"
    - "stackgan realistic image synthesis with stacked generative adversarial networks"
    - "shape inpainting using 3d generative adversarial network and recurrent convolutional networks"
    - "semantic image inpainting with deep generative models"
    - "context encoders feature learning by inpainting"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "adam a method for stochastic optimization"

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)