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: "personalizing human video pose estimation"
  Abstract: We propose a personalized ConvNet pose estimator that automatically adapts itself to the uniqueness of a person's appearance to improve pose estimation in long videos. We make the following contributions: (i) we show that given a few high-precision pose annotations, e.g. from a generic ConvNet pose estimator, additional annotations can be generated throughout the video using a combination of image-based matching for temporally distant frames, and dense optical flow for temporally local frames; (ii) we develop an occlusion aware self-evaluation model that is able to automatically select the high-quality and reject the erroneous additional annotations; and (iii) we demonstrate that these high-quality annotations can be used to fine-tune a ConvNet pose estimator and thereby personalize it to lock on to key discriminative features of the person's appearance. The outcome is a substantial improvement in the pose estimates for the target video using the personalized ConvNet compared to the original generic ConvNet. Our method outperforms the state of the art (including top ConvNet methods) by a large margin on two standard benchmarks, as well as on a new challenging YouTube video dataset. Furthermore, we show that training from the automatically generated annotations can be used to improve the performance of a generic ConvNet on other benchmarks.

Connected through hyperedge HE-1 (induced by source paper "unipose unified human pose estimation in single images and videos"):
  with members:
    - "res2net a new multi scale backbone architecture"
    - "cascade feature aggregation for human pose estimation"
    - "human pose estimation with spatial contextual information"
    - "deep high resolution representation learning for human pose estimation"
    - "lighttrack a generic framework for online top down human pose tracking"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "personlab person pose estimation and instance segmentation with a bottom up part based geometric embedding model"
    - "multi scale structure aware network for human pose estimation"

Connected through hyperedge HE-2 (induced by source paper "learning multi human optical flow"):
  with members:
    - "amass archive of motion capture as surface shapes"
    - "learning human optical flow"
    - "supervision by registration an unsupervised approach to improve the precision of facial landmark detectors"
    - "looking beyond appearances synthetic training data for deep cnns in re identification"
    - "pwc net cnns for optical flow using pyramid warping and cost volume"
    - "learning from synthetic humans"
    - "mask r cnn"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"

Connected through hyperedge HE-3 (induced by source paper "learning temporal pose estimation from sparsely labeled videos"):
  with members:
    - "learning correspondence from the cycle consistency of time"
    - "deep high resolution representation learning for human pose estimation"
    - "multi domain pose network for multi person pose estimation and tracking"
    - "pose flow efficient online pose tracking"
    - "self supervised learning of a facial attribute embedding from video"
    - "simple baselines for human pose estimation and tracking"
    - "thin slicing network a deep structured model for pose estimation in videos"
    - "focal loss for dense object detection"

Connected through hyperedge HE-4 (induced by source paper "semi and weakly supervised human pose estimation"):
  with members:
    - "harvesting multiple views for marker less 3d human pose annotations"
    - "hand keypoint detection in single images using multiview bootstrapping"
    - "convolutional pose machines"
    - "stacked hourglass networks for human pose estimation"
    - "structured feature learning for pose estimation"
    - "deepercut a deeper stronger and faster multi person pose estimation model"
    - "human pose estimation using deep consensus voting"
    - "chained predictions using convolutional neural networks"

Connected through hyperedge HE-5 (induced by source paper "learning to detect and track visible and occluded body joints in a virtual world"):
  with members:
    - "simple online and realtime tracking with a deep association metric"
    - "detect to track and track to detect"
    - "posetrack a benchmark for human pose estimation and tracking"
    - "tracking the untrackable learning to track multiple cues with long term dependencies"
    - "online multi object tracking using cnn based single object tracker with spatial temporal attention mechanism"
    - "convolutional pose machines"
    - "stacked hourglass networks for human pose estimation"
    - "posetrack joint multi person pose estimation and tracking"

Connected through hyperedge HE-6 (induced by source paper "video salient object detection via fully convolutional networks"):
  with members:
    - "deep cropping via attention box prediction and aesthetics assessment"
    - "deep contrast learning for salient object detection"
    - "saliency detection via combining region level and pixel level predictions with cnns"
    - "spatio temporal saliency networks for dynamic saliency prediction"
    - "learning video object segmentation from static images"
    - "two stream convolutional networks for dynamic saliency prediction"
    - "deepsaliency multi task deep neural network model for salient object detection"
    - "visual saliency based on multiscale deep features"

Connected through hyperedge HE-7 (induced by source paper "generating a fusion image one s identity and another s shape"):
  with members:
    - "pose guided person image generation"
    - "generative face completion"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "precomputed real time texture synthesis with markovian generative adversarial networks"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "rendergan generating realistic labeled data"
    - "learning from simulated and unsupervised images through adversarial training"

Connected through hyperedge HE-8 (induced by source paper "learning human optical flow"):
  with members:
    - "learning from synthetic humans"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"
    - "optical flow estimation using a spatial pyramid network"
    - "mot16 a benchmark for multi object tracking"
    - "a large dataset to train convolutional networks for disparity optical flow and scene flow estimation"
    - "virtual worlds as proxy for multi object tracking analysis"
    - "convolutional two stream network fusion for video action recognition"
    - "spatial transformer networks"

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)