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: "3d object reconstruction from hand object interactions"
  Abstract: Recent advances have enabled 3d object reconstruction approaches using a single off-the-shelf RGB-D camera. Although these approaches are successful for a wide range of object classes, they rely on stable and distinctive geometric or texture features. Many objects like mechanical parts, toys, household or decorative articles, however, are textureless and characterized by minimalistic shapes that are simple and symmetric. Existing in-hand scanning systems and 3d reconstruction techniques fail for such symmetric objects in the absence of highly distinctive features. In this work, we show that extracting 3d hand motion for in-hand scanning effectively facilitates the reconstruction of even featureless and highly symmetric objects and we present an approach that fuses the rich additional information of hands into a 3d reconstruction pipeline, significantly contributing to the state-of-the-art of in-hand scanning.

Connected through hyperedge HE-1 (induced by source paper "generalized feedback loop for joint hand object pose estimation"):
  with members:
    - "deepim deep iterative matching for 6d pose estimation"
    - "pose guided structured region ensemble network for cascaded hand pose estimation"
    - "learning to estimate 3d hand pose from single rgb images"
    - "hand3d hand pose estimation using 3d neural network"
    - "region ensemble network improving convolutional network for hand pose estimation"
    - "deepprior improving fast and accurate 3d hand pose estimation"
    - "bb8 a scalable accurate robust to partial occlusion method for predicting the 3d poses of challenging objects without using depth"
    - "dynamics based 3d skeletal hand tracking"

Connected through hyperedge HE-2 (induced by source paper "honnotate a method for 3d annotation of hand and objects poses"):
  with members:
    - "freihand a dataset for markerless capture of hand pose and shape from single rgb images"
    - "learning single image 3d reconstruction by generative modelling of shape pose and shading"
    - "learning to estimate pose and shape of hand held objects from rgb images"
    - "learning joint reconstruction of hands and manipulated objects"
    - "h o unified egocentric recognition of 3d hand object poses and interactions"
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "domain transfer for 3d pose estimation from color images without manual annotations"
    - "total capture a 3d deformation model for tracking faces hands and bodies"

Connected through hyperedge HE-3 (induced by source paper "random forest with learned representations for semantic segmentation"):
  with members:
    - "ocnet object context network for scene parsing"
    - "depth adaptive deep neural network for semantic segmentation"
    - "ccnet criss cross attention for semantic segmentation"
    - "non local neural networks"
    - "active convolution learning the shape of convolution for image classification"
    - "deformable convolutional networks"
    - "icnet for real time semantic segmentation on high resolution images"
    - "enet a deep neural network architecture for real time semantic segmentation"

Connected through hyperedge HE-4 (induced by source paper "srda generating instance segmentation annotation via scanning reasoning and domain adaptation"):
  with members:
    - "learning to discover cross domain relations with generative adversarial networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "adversarially tuned scene generation"
    - "synthesizing training data for object detection in indoor scenes"
    - "one sided unsupervised domain mapping"
    - "photographic image synthesis with cascaded refinement networks"
    - "dualgan unsupervised dual learning for image to image translation"
    - "mask r cnn"

Connected through hyperedge HE-5 (induced by source paper "depth adaptive deep neural network for semantic segmentation"):
  with members:
    - "active convolution learning the shape of convolution for image classification"
    - "deformable convolutional networks"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "robust 3d hand pose estimation in single depth images from single view cnn to multi view cnns"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "context matters refining object detection in video with recurrent neural networks"
    - "human pose estimation from depth images via inference embedded multi task learning"
    - "you only look once unified real time object detection"

Connected through hyperedge HE-6 (induced by source paper "real time hand tracking under occlusion from an egocentric rgb d sensor"):
  with members:
    - "crossing nets combining gans and vaes with a shared latent space for hand pose estimation"
    - "an analysis of deep neural network models for practical applications"
    - "training a feedback loop for hand pose estimation"
    - "egocap egocentric marker less motion capture with two fisheye cameras"
    - "robust 3d hand pose estimation in single depth images from single view cnn to multi view cnns"
    - "spatial attention deep net with partial pso for hierarchical hybrid hand pose estimation"
    - "monocular 3d human pose estimation in the wild using improved cnn supervision"
    - "highway and residual networks learn unrolled iterative estimation"

Connected through hyperedge HE-7 (induced by source paper "real time joint tracking of a hand manipulating an object from rgb d input"):
  with members:
    - "dynamics based 3d skeletal hand tracking"
    - "gogma globally optimal gaussian mixture alignment"
    - "capturing hands in action using discriminative salient points and physics simulation"
    - "fast and robust hand tracking using detection guided optimization"
    - "capturing hand motion with an rgb d sensor fusing a generative model with salient points"

Connected through hyperedge HE-8 (induced by source paper "hand segmentation for hand object interaction from depth map"):
  with members:
    - "depth adaptive deep neural network for semantic segmentation"
    - "real time sign language fingerspelling recognition using convolutional neural networks from depth map"
    - "efficient hand articulations tracking using adaptive hand model and depth map"
    - "fully convolutional networks for semantic segmentation"

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)