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: "intel realsense stereoscopic depth cameras"
  Abstract: We present a comprehensive overview of the stereoscopic Intel RealSense RGBD imaging systems. We discuss these systems' mode-of-operation, functional behavior and include models of their expected performance, shortcomings, and limitations. We provide information about the systems' optical characteristics, their correlation algorithms, and how these properties can affect different applications, including 3D reconstruction and gesture recognition. Our discussion covers the Intel RealSense R200 and the Intel RealSense D400 (formally RS400).

Connected through hyperedge HE-1 (induced by source paper "infrared and 3d skeleton feature fusion for rgb d action recognition"):
  with members:
    - "actional structural graph convolutional networks for skeleton based action recognition"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "two stream adaptive graph convolutional networks for skeleton based action recognition"
    - "view adaptive neural networks for high performance skeleton based human action recognition"
    - "how powerful are graph neural networks"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "skeleton based action recognition using translation scale invariant image mapping and multi scale deep cnn"

Connected through hyperedge HE-2 (induced by source paper "vision based robot manipulation learning via human demonstrations"):
  with members:
    - "robot learning and execution of collaborative manipulation plans from youtube videos"
    - "learning actions from human demonstration video for robotic manipulation"
    - "an algorithmic perspective on imitation learning"
    - "one shot observation learning using visual activity features"
    - "one shot imitation from observing humans via domain adaptive meta learning"
    - "interactively picking real world objects with unconstrained spoken language instructions"
    - "one shot visual imitation learning via meta learning"
    - "imitation from observation learning to imitate behaviors from raw video via context translation"

Connected through hyperedge HE-3 (induced by source paper "an efficient sampling based method for online informative path planning in unknown environments"):
  with members:
    - "safe local exploration for replanning in cluttered unknown environments for microaerial vehicles"
    - "voxblox incremental 3d euclidean signed distance fields for on board mav planning"
    - "sampling based algorithms for optimal motion planning"

Connected through hyperedge HE-4 (induced by source paper "synthetic defocus and look ahead autofocus for casual videography"):
  with members:
    - "sg fcn a motion and memory based deep learning model for video saliency detection"
    - "deeply supervised salient object detection with short connections"
    - "synthetic depth of field with a single camera mobile phone"
    - "deeplens shallow depth of field from a single image"
    - "revisiting video saliency a large scale benchmark and a new model"
    - "looking to listen at the cocktail party a speaker independent audio visual model for speech separation"
    - "scanner efficient video analysis at scale"
    - "tracking emerges by colorizing videos"

Connected through hyperedge HE-5 (induced by source paper "3d bird s eye view instance segmentation"):
  with members:
    - "dilated point convolutions on the receptive field of point convolutions"
    - "learning to cluster for proposal free instance segmentation"
    - "3dmv joint 3d multi view prediction for 3d semantic scene segmentation"
    - "fully convolutional point networks for large scale point clouds"
    - "dynamic graph cnn for learning on point clouds"
    - "gspn generative shape proposal network for 3d instance segmentation in point cloud"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "know what your neighbors do 3d semantic segmentation of point clouds"

Connected through hyperedge HE-6 (induced by source paper "pose guided structured region ensemble network for cascaded hand pose estimation"):
  with members:
    - "deep alignment network a convolutional neural network for robust face alignment"
    - "region ensemble network improving convolutional network for hand pose estimation"
    - "end to end global to local cnn learning for hand pose recovery in depth data"
    - "dynamics based 3d skeletal hand tracking"
    - "motion feature augmented recurrent neural network for skeleton based dynamic hand gesture recognition"
    - "mask r cnn"
    - "training a feedback loop for hand pose estimation"
    - "robust 3d hand pose estimation in single depth images from single view cnn to multi view cnns"

Connected through hyperedge HE-7 (induced by source paper "bi stream pose guided region ensemble network for fingertip localization from stereo images"):
  with members:
    - "pose guided structured region ensemble network for cascaded hand pose estimation"
    - "interactive hand pose estimation boosting accuracy in localizing extended finger joints"
    - "crossing nets combining gans and vaes with a shared latent space for hand pose estimation"
    - "back to rgb 3d tracking of hands and hand object interactions based on short baseline stereo"
    - "region ensemble network improving convolutional network for hand pose estimation"
    - "bighand2 2m benchmark hand pose dataset and state of the art analysis"
    - "depth based 3d hand pose estimation from current achievements to future goals"
    - "training a feedback loop for hand pose estimation"

Connected through hyperedge HE-8 (induced by source paper "direct fitting of gaussian mixture models"):
  with members:
    - "fast monte carlo localization on aerial vehicles using approximate continuous belief representations"
    - "point set registration coherent point drift"

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)