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: "human pose estimation from depth images via inference embedded multi task learning"
  Abstract: Human pose estimation (i.e., locating the body parts / joints of a person) is a fundamental problem in human-computer interaction and multimedia applications. Significant progress has been made based on the development of depth sensors, i.e., accessible human pose prediction from still depth images [32]. However, most of the existing approaches to this problem involve several components/models that are independently designed and optimized, leading to suboptimal performances. In this paper, we propose a novel inference-embedded multi-task learning framework for predicting human pose from still depth images, which is implemented with a deep architecture of neural networks. Specifically, we handle two cascaded tasks: i) generating the heat (confidence) maps of body parts via a fully convolutional network (FCN); ii) seeking the optimal configuration of body parts based on the detected body part proposals via an inference built-in MatchNet [10], which measures the appearance and geometric kinematic compatibility of body parts and embodies the dynamic programming inference as an extra network layer. These two tasks are jointly optimized. Our extensive experiments show that the proposed deep model significantly improves the accuracy of human pose estimation over other several state-of-the-art methods or SDKs. We also release a large-scale dataset for comparison, which includes 100K depth images under challenging scenarios.

Connected through hyperedge HE-1 (induced by source paper "a2j anchor to joint regression network for 3d articulated pose estimation from a single depth image"):
  with members:
    - "pose guided structured region ensemble network for cascaded hand pose estimation"
    - "simple baselines for human pose estimation and tracking"
    - "crossing nets combining gans and vaes with a shared latent space for hand pose estimation"
    - "hand3d hand pose estimation using 3d neural network"
    - "region ensemble network improving convolutional network for hand pose estimation"
    - "bighand2 2m benchmark hand pose dataset and state of the art analysis"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "deepprior improving fast and accurate 3d hand pose estimation"

Connected through hyperedge HE-2 (induced by source paper "efficient convolutional neural networks for depth based multi person pose estimation"):
  with members:
    - "knowledge transfer with jacobian matching"
    - "learning to detect and track visible and occluded body joints in a virtual world"
    - "real time convolutional networks for depth based human pose estimation"
    - "densepose dense human pose estimation in the wild"
    - "domain adaptation for visual applications a comprehensive survey"
    - "an analysis of deep neural network models for practical applications"
    - "cycada cycle consistent adversarial domain adaptation"
    - "lstm pose machines"

Connected through hyperedge HE-3 (induced by source paper "cross modal attentional context learning for rgb d object detection"):
  with members:
    - "contrast oriented deep neural networks for salient object detection"
    - "dsod learning deeply supervised object detectors from scratch"
    - "instance level salient object segmentation"
    - "lstm cf unifying context modeling and fusion with lstms for rgb d scene labeling"
    - "deep feature based contextual model for object detection"
    - "ssd single shot multibox detector"
    - "attentive contexts for object detection"
    - "visual saliency detection based on multiscale deep cnn features"

Connected through hyperedge HE-4 (induced by source paper "learning task relatedness in multi task learning for images in context"):
  with members:
    - "taskonomy disentangling task transfer learning"
    - "gnas a greedy neural architecture search method for multi attribute learning"
    - "sketchparse towards rich descriptions for poorly drawn sketches using multi task hierarchical deep networks"
    - "an overview of multi task learning in deep neural networks"
    - "omniart multi task deep learning for artistic data analysis"
    - "deep cross residual learning for multitask visual recognition"
    - "distributed multi task relationship learning"
    - "deep multi task representation learning a tensor factorisation approach"

Connected through hyperedge HE-5 (induced by source paper "what twitter profile and posted images reveal about depression and anxiety"):
  with members:
    - "understanding and measuring psychological stress using social media"
    - "wearing many social hats how different are your different social network personae"
    - "what your facebook profile picture reveals about your personality"
    - "instagram photos reveal predictive markers of depression"
    - "greysanatomy vs yankees demographics and hashtag use on twitter"
    - "cooperative phoneotypes exploring phone based behavioral markers of cooperation"
    - "photo aesthetics ranking network with attributes and content adaptation"
    - "social media image analysis for public health"

Connected through hyperedge HE-6 (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"
    - "3d object reconstruction from hand object interactions"
    - "you only look once unified real time object detection"

Connected through hyperedge HE-7 (induced by source paper "real time convolutional networks for depth based human pose estimation"):
  with members:
    - "an analysis of deep neural network models for practical applications"
    - "a multi view rgb d approach for human pose estimation in operating rooms"
    - "convolutional pose machines"
    - "stacked hourglass networks for human pose estimation"
    - "learning from simulated and unsupervised images through adversarial training"
    - "deepercut a deeper stronger and faster multi person pose estimation model"
    - "human pose estimation via convolutional part heatmap regression"
    - "towards viewpoint invariant 3d human pose estimation"

Connected through hyperedge HE-8 (induced by source paper "structure preserving image super resolution via contextualized multitask learning"):
  with members:
    - "attention aware face hallucination via deep reinforcement learning"
    - "perceptual losses for real time style transfer and super resolution"
    - "accelerating the super resolution convolutional neural network"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "factorized convolutional neural networks"
    - "local and holistic structure preserving image super resolution via deep joint component learning"
    - "real time single image and video super resolution using an efficient sub pixel convolutional neural network"
    - "raisr rapid and accurate image super resolution"

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)