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: "occlusion aware hand pose estimation using hierarchical mixture density network"
  Abstract: Hand pose estimation is to predict the pose parameters representing a 3D hand model, such as locations of hand joints. This problem is very challenging due to large changes in viewpoints and articulations, and intense self-occlusions, etc. Many researchers have investigated the problem from both aspects of input feature learning and output prediction modelling. Though effective, most of the existing discriminative methods only give a deterministic estimation of target poses. Also, due to their single-value mapping intrinsic, they fail to adequately handle self-occlusion problems, where occluded joints present multiple modes. In this paper, we tackle the self-occlusion issue and provide a complete description of observed poses given an input depth image through a hierarchical mixture density network (HMDN) framework. In particular, HMDN leverages the state-of-the-art CNN module to facilitate feature learning, while proposes a density in a two-level hierarchy to reconcile single-valued and multi-valued mapping in the output. The whole framework is naturally end-to-end trainable with a mixture of two differentiable density functions. HMDN produces interpretable and diverse candidate samples, and significantly outperforms the state-of-the-art algorithms on benchmarks that exhibit occlusions.

Connected through hyperedge HE-1 (induced by source paper "finding interesting frames in deep video analytics a top k approach"):
  with members:
    - "blazeit optimizing declarative aggregation and limit queries for neural network based video analytics"
    - "yolov3 an incremental improvement"
    - "focus querying large video datasets with low latency and low cost"
    - "scanner efficient video analysis at scale"
    - "face detection using deep learning an improved faster rcnn approach"
    - "abnormality detection and localization in chest x rays using deep convolutional neural networks"
    - "noscope optimizing neural network queries over video at scale"
    - "mask r cnn"

Connected through hyperedge HE-2 (induced by source paper "an end to end framework for unsupervised pose estimation of occluded pedestrians"):
  with members:
    - "semantics guided neural networks for efficient skeleton based human action recognition"
    - "deep high resolution representation learning for human pose estimation"
    - "occlusion aware r cnn detecting pedestrians in a crowd"
    - "simple baselines for human pose estimation and tracking"
    - "crowdpose efficient crowded scenes pose estimation and a new benchmark"
    - "multiposenet fast multi person pose estimation using pose residual network"
    - "path aggregation network for instance segmentation"
    - "citypersons a diverse dataset for pedestrian detection"

Connected through hyperedge HE-3 (induced by source paper "mixture dense regression for object detection and human pose estimation"):
  with members:
    - "rethinking on multi stage networks for human pose estimation"
    - "lvis a dataset for large vocabulary instance segmentation"
    - "overcoming limitations of mixture density networks a sampling and fitting framework for multimodal future prediction"
    - "gaussian yolov3 an accurate and fast object detector using localization uncertainty for autonomous driving"
    - "feature selective anchor free module for single shot object detection"
    - "unsupervised discovery of parts structure and dynamics"
    - "pifpaf composite fields for human pose estimation"
    - "scale aware trident networks for object detection"

Connected through hyperedge HE-4 (induced by source paper "h o unified egocentric recognition of 3d hand object poses and interactions"):
  with members:
    - "learning joint reconstruction of hands and manipulated objects"
    - "scaling egocentric vision the epic kitchens dataset"
    - "hand pose estimation via latent 2 5d heatmap regression"
    - "cross modal deep variational hand pose estimation"
    - "object level visual reasoning in videos"
    - "detecting and recognizing human object interactions"
    - "learning to estimate 3d hand pose from single rgb images"
    - "from lifestyle vlogs to everyday interactions"

Connected through hyperedge HE-5 (induced by source paper "normalized diversification"):
  with members:
    - "multimodal unsupervised image to image translation"
    - "spectral normalization for generative adversarial networks"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "cross modal deep variational hand pose estimation"
    - "began boundary equilibrium generative adversarial networks"
    - "progressive growing of gans for improved quality stability and variation"
    - "learning to estimate 3d hand pose from single rgb images"
    - "pacgan the power of two samples in generative adversarial networks"

Connected through hyperedge HE-6 (induced by source paper "generating multiple hypotheses for 3d human pose estimation with mixture density network"):
  with members:
    - "exploiting temporal information for 3d human pose estimation"
    - "towards 3d human pose estimation in the wild a weakly supervised approach"
    - "harvesting multiple views for marker less 3d human pose annotations"
    - "a simple yet effective baseline for 3d human pose estimation"
    - "generating multiple diverse hypotheses for human 3d pose consistent with 2d joint detections"
    - "convolutional pose machines"
    - "stacked hourglass networks for human pose estimation"
    - "coarse to fine volumetric prediction for single image 3d human pose"

Connected through hyperedge HE-7 (induced by source paper "grabar occlusion aware grabbing virtual objects in ar"):
  with members:
    - "3d hand shape and pose estimation from a single rgb image"
    - "3d hand shape and pose from images in the wild"
    - "pushing the envelope for rgb based dense 3d hand pose estimation via neural rendering"
    - "deep ordinal regression network for monocular depth estimation"
    - "deephps end to end estimation of 3d hand pose and shape by learning from synthetic depth"
    - "learning to infer the depth map of a hand from its color image"
    - "depth estimation using structured light flow analysis of projected pattern flow on an object s surface"
    - "learning to estimate 3d hand pose from single rgb images"

Connected through hyperedge HE-8 (induced by source paper "deephps end to end estimation of 3d hand pose and shape by learning from synthetic depth"):
  with members:
    - "pose guided structured region ensemble network for cascaded hand pose estimation"
    - "crossing nets combining gans and vaes with a shared latent space for hand pose estimation"
    - "learning to estimate 3d hand pose from single rgb images"
    - "using a single rgb frame for real time 3d hand pose estimation in the wild"
    - "v2v posenet voxel to voxel prediction network for accurate 3d hand and human pose estimation from a single depth map"
    - "hand3d hand pose estimation using 3d neural network"
    - "simultaneous hand pose and skeleton bone lengths estimation from a single depth image"
    - "region ensemble network improving convolutional network for hand pose estimation"

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)