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: "toward marker free 3d pose estimation in lifting a deep multi view solution"
  Abstract: Lifting is a common manual material handling task performed in the workplaces. It is considered as one of the main risk factors for Work-related Musculoskeletal Disorders. To improve work place safety, it is necessary to assess musculoskeletal and biomechanical risk exposures associated with these tasks, which requires very accurate 3D pose. Existing approaches mainly utilize marker-based sensors to collect 3D information. However, these methods are usually expensive to setup, time-consuming in process, and sensitive to the surrounding environment. In this study, we propose a multi-view based deep perceptron approach to address aforementioned limitations. Our approach consists of two modules: a "view-specific perceptron" network extracts rich information independently from the image of view, which includes both 2D shape and hierarchical texture information; while a "multi-view integration" network synthesizes information from all available views to predict accurate 3D pose. To fully evaluate our approach, we carried out comprehensive experiments to compare different variants of our design. The results prove that our approach achieves comparable performance with former marker-based methods, i.e. an average error of $14.72 \pm 2.96$ mm on the lifting dataset. The results are also compared with state-of-the-art methods on HumanEva-I dataset, which demonstrates the superior performance of our approach.

Connected through hyperedge HE-1 (induced by source paper "automatic health problem detection from gait videos using deep neural networks"):
  with members:
    - "deep cross polarimetric thermal to visible face recognition"
    - "deep sketch photo face recognition assisted by facial attributes"
    - "harvesting multiple views for marker less 3d human pose annotations"
    - "a simple yet effective baseline for 3d human pose estimation"
    - "convolutional pose machines"
    - "stacked hourglass networks for human pose estimation"
    - "coarse to fine volumetric prediction for single image 3d human pose"
    - "learning to fuse 2d and 3d image cues for monocular body pose estimation"

Connected through hyperedge HE-2 (induced by source paper "toward ergonomic risk prediction via segmentation of indoor object manipulation actions using spatiotemporal convolutional networks"):
  with members:
    - "stacked spatio temporal graph convolutional networks for action segmentation"
    - "connectionist temporal modeling for weakly supervised action labeling"
    - "temporal convolutional networks for action segmentation and detection"
    - "wavenet a generative model for raw audio"
    - "an end to end generative framework for video segmentation and recognition"
    - "p cnn pose based cnn features for action recognition"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-3 (induced by source paper "attribute guided deep polarimetric thermal to visible face recognition"):
  with members:
    - "generative adversarial network based synthesis of visible faces from polarimetric thermal faces"
    - "perceptual losses for real time style transfer and super resolution"
    - "face attribute prediction using off the shelf cnn features"
    - "image to image translation with conditional adversarial networks"
    - "u net convolutional networks for biomedical image segmentation"
    - "multi task cnn model for attribute prediction"
    - "deep perceptual mapping for thermal to visible face recognition"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "predicting ergonomic risks during indoor object manipulation using spatiotemporal convolutional networks"):
  with members:
    - "connectionist temporal modeling for weakly supervised action labeling"
    - "temporal convolutional networks for action segmentation and detection"
    - "wavenet a generative model for raw audio"
    - "an end to end generative framework for video segmentation and recognition"
    - "p cnn pose based cnn features for action recognition"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"
    - "return of the devil in the details delving deep into convolutional nets"

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)