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: "lstm pose machines"
  Abstract: We observed that recent state-of-the-art results on single image human pose estimation were achieved by multi-stage Convolution Neural Networks (CNN). Notwithstanding the superior performance on static images, the application of these models on videos is not only computationally intensive, it also suffers from performance degeneration and flicking. Such suboptimal results are mainly attributed to the inability of imposing sequential geometric consistency, handling severe image quality degradation (e.g. motion blur and occlusion) as well as the inability of capturing the temporal correlation among video frames. In this paper, we proposed a novel recurrent network to tackle these problems. We showed that if we were to impose the weight sharing scheme to the multi-stage CNN, it could be re-written as a Recurrent Neural Network (RNN). This property decouples the relationship among multiple network stages and results in significantly faster speed in invoking the network for videos. It also enables the adoption of Long Short-Term Memory (LSTM) units between video frames. We found such memory augmented RNN is very effective in imposing geometric consistency among frames. It also well handles input quality degradation in videos while successfully stabilizes the sequential outputs. The experiments showed that our approach significantly outperformed current state-of-the-art methods on two large-scale video pose estimation benchmarks. We also explored the memory cells inside the LSTM and provided insights on why such mechanism would benefit the prediction for video-based pose estimations.

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 "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"
    - "v2v posenet voxel to voxel prediction network for accurate 3d hand and human pose estimation from a single depth map"

Connected through hyperedge HE-3 (induced by source paper "temporally coherent full 3d mesh human pose recovery from monocular video"):
  with members:
    - "amass archive of motion capture as surface shapes"
    - "learning to reconstruct people in clothing from a single rgb camera"
    - "simulcap single view human performance capture with cloth simulation"
    - "video based reconstruction of 3d people models"
    - "3d human pose estimation in the wild by adversarial learning"
    - "neural body fitting unifying deep learning and model based human pose and shape estimation"
    - "drpose3d depth ranking in 3d human pose estimation"
    - "detailed human avatars from monocular video"

Connected through hyperedge HE-4 (induced by source paper "object parsing in sequences using coordconv gated recurrent networks"):
  with members:
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "instance segmentation and tracking with cosine embeddings and recurrent hourglass networks"
    - "thin slicing network a deep structured model for pose estimation in videos"
    - "recurrent 3d pose sequence machines"
    - "convolutional pose machines"
    - "stacked hourglass networks for human pose estimation"
    - "warpnet weakly supervised matching for single view reconstruction"
    - "deep supervision with shape concepts for occlusion aware 3d object parsing"

Connected through hyperedge HE-5 (induced by source paper "dynamic kernel distillation for efficient pose estimation in videos"):
  with members:
    - "simple baselines for human pose estimation and tracking"
    - "began boundary equilibrium generative adversarial networks"
    - "thin slicing network a deep structured model for pose estimation in videos"
    - "end to end representation learning for correlation filter based tracking"
    - "self adversarial training for human pose estimation"
    - "adversarial posenet a structure aware convolutional network for human pose estimation"
    - "detect and track efficient pose estimation in videos"
    - "convolutional pose machines"

Connected through hyperedge HE-6 (induced by source paper "identifying emotions from walking using affective and deep features"):
  with members:
    - "modeling data driven dominance traits for virtual characters using gait analysis"
    - "fva modeling perceived friendliness of virtual agents using movement characteristics"
    - "pose guided human video generation"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "deep video generation prediction and completion of human action sequences"
    - "lstm a search space odyssey"
    - "deep representation learning for human motion prediction and classification"
    - "learning 3d human pose from structure and motion"

Connected through hyperedge HE-7 (induced by source paper "end to end learning for graph decomposition"):
  with members:
    - "end to end learning of multi sensor 3d tracking by detection"
    - "personlab person pose estimation and instance segmentation with a bottom up part based geometric embedding model"
    - "jointly optimize data augmentation and network training adversarial data augmentation in human pose estimation"
    - "thin slicing network a deep structured model for pose estimation in videos"
    - "optnet differentiable optimization as a layer in neural networks"
    - "towards accurate multi person pose estimation in the wild"
    - "deep network flow for multi object tracking"
    - "mask r cnn"

Connected through hyperedge HE-8 (induced by source paper "efficient online multi person 2d pose tracking with recurrent spatio temporal affinity fields"):
  with members:
    - "multi domain pose network for multi person pose estimation and tracking"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "pose flow efficient online pose tracking"
    - "joint flow temporal flow fields for multi person tracking"
    - "personlab person pose estimation and instance segmentation with a bottom up part based geometric embedding model"
    - "simple baselines for human pose estimation and tracking"
    - "densepose dense human pose estimation in the wild"
    - "thin slicing network a deep structured model for pose estimation in videos"

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)