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: "flowing convnets for human pose estimation in videos"
  Abstract: The objective of this work is human pose estimation in videos, where multiple frames are available. We investigate a ConvNet architecture that is able to benefit from temporal context by combining information across the multiple frames using optical flow. #R##N#To this end we propose a network architecture with the following novelties: (i) a deeper network than previously investigated for regressing heatmaps; (ii) spatial fusion layers that learn an implicit spatial model; (iii) optical flow is used to align heatmap predictions from neighbouring frames; and (iv) a final parametric pooling layer which learns to combine the aligned heatmaps into a pooled confidence map. #R##N#We show that this architecture outperforms a number of others, including one that uses optical flow solely at the input layers, one that regresses joint coordinates directly, and one that predicts heatmaps without spatial fusion. #R##N#The new architecture outperforms the state of the art by a large margin on three video pose estimation datasets, including the very challenging Poses in the Wild dataset, and outperforms other deep methods that don't use a graphical model on the single-image FLIC benchmark (and also Chen & Yuille and Tompson et al. in the high precision region).

Connected through hyperedge HE-1 (induced by source paper "hierarchical binary cnns for landmark localization with limited resources"):
  with members:
    - "how far are we from solving the 2d 3d face alignment problem and a dataset of 230 000 3d facial landmarks"
    - "godp globally optimized dual pathway deep network architecture for facial landmark localization in the wild"
    - "binarized convolutional landmark localizers for human pose estimation and face alignment with limited resources"
    - "convolutional pose machines"
    - "stacked hourglass networks for human pose estimation"
    - "dorefa net training low bitwidth convolutional neural networks with low bitwidth gradients"
    - "deep neural networks are robust to weight binarization and other non linear distortions"
    - "aggregated residual transformations for deep neural networks"

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 "unsupervised disentanglement of pose appearance and background from images and videos"):
  with members:
    - "unsupervised part based disentangling of object shape and appearance"
    - "neural scene decomposition for multi person motion capture"
    - "semantic image synthesis with spatially adaptive normalization"
    - "video interpolation and prediction with unsupervised landmarks"
    - "discovery of latent 3d keypoints via end to end geometric reasoning"
    - "synthesizing images of humans in unseen poses"
    - "unsupervised geometry aware representation for 3d human pose estimation"
    - "unsupervised learning of object landmarks through conditional image generation"

Connected through hyperedge HE-4 (induced by source paper "hand priming in object localization for assistive egocentric vision"):
  with members:
    - "digging deeper into egocentric gaze prediction"
    - "h o unified egocentric recognition of 3d hand object poses and interactions"
    - "scaling egocentric vision the epic kitchens dataset"
    - "vizwiz grand challenge answering visual questions from blind people"
    - "predicting gaze in egocentric video by learning task dependent attention transition"
    - "next active object prediction from egocentric videos"
    - "mask r cnn"
    - "fully convolutional instance aware semantic segmentation"

Connected through hyperedge HE-5 (induced by source paper "joint coco and mapillary workshop at iccv 2019 keypoint detection challenge track technical report distribution aware coordinate representation for human pose estimation"):
  with members:
    - "deep high resolution representation learning for human pose estimation"
    - "simple baselines for human pose estimation and tracking"
    - "sniper efficient multi scale training"
    - "learning feature pyramids for human pose estimation"
    - "cascaded pyramid network for multi person pose estimation"
    - "convolutional pose machines"
    - "stacked hourglass networks for human pose estimation"
    - "joint training of a convolutional network and a graphical model for human pose estimation"

Connected through hyperedge HE-6 (induced by source paper "the ladder algorithm finding repetitive structures in medical images by induction"):
  with members:
    - "deepspine automated lumbar vertebral segmentation disc level designation and spinal stenosis grading using deep learning"
    - "inductive visual localisation factorised training for superior generalisation"
    - "recurrent instance segmentation"
    - "adam a method for stochastic optimization"
    - "joint training of a convolutional network and a graphical model for human pose estimation"
    - "return of the devil in the details delving deep into convolutional nets"
    - "learning human pose estimation features with convolutional networks"

Connected through hyperedge HE-7 (induced by source paper "deep high resolution representation learning for human pose estimation"):
  with members:
    - "quantized densely connected u nets for efficient landmark localization"
    - "igcv3 interleaved low rank group convolutions for efficient deep neural networks"
    - "pose flow efficient online pose tracking"
    - "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"
    - "jointly optimize data augmentation and network training adversarial data augmentation in human pose estimation"
    - "simple baselines for human pose estimation and tracking"
    - "multiposenet fast multi person pose estimation using pose residual network"

Connected through hyperedge HE-8 (induced by source paper "learning landmarks from unaligned data using image translation"):
  with members:
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "learning to read by spelling towards unsupervised text recognition"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "unsupervised learning of object landmarks through conditional image generation"
    - "self supervised learning of a facial attribute embedding from video"
    - "wing loss for robust facial landmark localisation with convolutional neural networks"
    - "3d human pose estimation in the wild by adversarial learning"
    - "unsupervised discovery of object landmarks as structural representations"

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)