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: "rule of thumb deep derotation for improved fingertip detection"
  Abstract: We investigate a novel global orientation regression approach for articulated objects using a deep convolutional neural network. This is integrated with an in-plane image derotation scheme, DeROT, to tackle the problem of per-frame fingertip detection in depth images. The method reduces the complexity of learning in the space of articulated poses which is demonstrated by using two distinct state-of-the-art learning based hand pose estimation methods applied to fingertip detection. Significant classification improvements are shown over the baseline implementation. Our framework involves no tracking, kinematic constraints or explicit prior model of the articulated object in hand. To support our approach we also describe a new pipeline for high accuracy magnetic annotation and labeling of objects imaged by a depth camera.

Connected through hyperedge HE-1 (induced by source paper "single image 3d hand reconstruction with mesh convolutions"):
  with members:
    - "meshgan non linear 3d morphable models of faces"
    - "neural 3d morphable models spiral convolutional networks for 3d shape representation learning and generation"
    - "end to end hand mesh recovery from a monocular rgb image"
    - "learning joint reconstruction of hands and manipulated objects"
    - "3d hand shape and pose from images in the wild"
    - "dense 3d face decoding over 2500fps joint texture shape convolutional mesh decoders"
    - "generating 3d faces using convolutional mesh autoencoders"
    - "learning to estimate 3d human pose and shape from a single color image"

Connected through hyperedge HE-2 (induced by source paper "hand pose estimation a survey"):
  with members:
    - "augmented skeleton space transfer for depth based hand pose estimation"
    - "cross modal deep variational hand pose estimation"
    - "learning to estimate 3d hand pose from single rgb images"
    - "bighand2 2m benchmark hand pose dataset and state of the art analysis"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "ganerated hands for real time 3d hand tracking from monocular rgb"
    - "hand keypoint detection in single images using multiview bootstrapping"

Connected through hyperedge HE-3 (induced by source paper "busyhands a hand tool interaction database for assembly tasks semantic segmentation"):
  with members:
    - "analysis of hand segmentation in the wild"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "learning to estimate 3d hand pose from single rgb images"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "hand keypoint detection in single images using multiview bootstrapping"
    - "rethinking atrous convolution for semantic image segmentation"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "full resolution residual networks for semantic segmentation in street scenes"

Connected through hyperedge HE-4 (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"
    - "intel realsense stereoscopic depth cameras"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "contextual attention for hand detection in the wild"):
  with members:
    - "busyhands a hand tool interaction database for assembly tasks semantic segmentation"
    - "handseg an automatically labeled dataset for hand segmentation from depth images"
    - "non local neural networks"
    - "learning to estimate 3d hand pose from single rgb images"
    - "squeeze and excitation networks"
    - "hand keypoint detection in single images using multiview bootstrapping"
    - "mask r cnn"
    - "spatial transformer networks"

Connected through hyperedge HE-6 (induced by source paper "towards markerless grasp capture"):
  with members:
    - "contactdb analyzing and predicting grasp contact via thermal imaging"
    - "contactgrasp functional multi finger grasp synthesis from contact"
    - "learning joint reconstruction of hands and manipulated objects"
    - "h o unified egocentric recognition of 3d hand object poses and interactions"
    - "bighand2 2m benchmark hand pose dataset and state of the art analysis"
    - "hand keypoint detection in single images using multiview bootstrapping"
    - "first person hand action benchmark with rgb d videos and 3d hand pose annotations"
    - "spatial attention deep net with partial pso for hierarchical hybrid hand pose estimation"

Connected through hyperedge HE-7 (induced by source paper "hand segmentation and fingertip tracking from depth camera images using deep convolutional neural network and multi task segnet"):
  with members:
    - "how far are we from solving pedestrian detection"
    - "yolo9000 better faster stronger"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-8 (induced by source paper "learning pose specific representations by predicting different views"):
  with members:
    - "crossing nets combining gans and vaes with a shared latent space for hand pose estimation"
    - "towards 3d human pose estimation in the wild a weakly supervised approach"
    - "unsupervised learning of depth and ego motion from video"
    - "deepprior improving fast and accurate 3d hand pose estimation"
    - "towards good practices for deep 3d hand pose estimation"
    - "dynamics based 3d skeletal hand tracking"
    - "representation learning by learning to count"
    - "multi view supervision for single view reconstruction via differentiable ray consistency"

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)