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 motion analysis with deep metric learning"
  Abstract: Effectively measuring the similarity between two human motions is necessary for several computer vision tasks such as gait analysis, person identi- fication and action retrieval. Nevertheless, we believe that traditional approaches such as L2 distance or Dynamic Time Warping based on hand-crafted local pose metrics fail to appropriately capture the semantic relationship across motions and, as such, are not suitable for being employed as metrics within these tasks. This work addresses this limitation by means of a triplet-based deep metric learning specifically tailored to deal with human motion data, in particular with the prob- lem of varying input size and computationally expensive hard negative mining due to motion pair alignment. Specifically, we propose (1) a novel metric learn- ing objective based on a triplet architecture and Maximum Mean Discrepancy; as well as, (2) a novel deep architecture based on attentive recurrent neural networks. One benefit of our objective function is that it enforces a better separation within the learned embedding space of the different motion categories by means of the associated distribution moments. At the same time, our attentive recurrent neural network allows processing varying input sizes to a fixed size of embedding while learning to focus on those motion parts that are semantically distinctive. Our ex- periments on two different datasets demonstrate significant improvements over conventional human motion metrics.

Connected through hyperedge HE-1 (induced by source paper "a review of computational approaches for evaluation of rehabilitation exercises"):
  with members:
    - "a deep learning framework for assessing physical rehabilitation exercises"
    - "parkinson s disease assessment from a wrist worn wearable sensor in free living conditions deep ensemble learning and visualization"
    - "time series segmentation through automatic feature learning"
    - "shoulder physiotherapy exercise recognition machine learning the inertial signals from a smartwatch"
    - "using inertial sensors for position and orientation estimation"
    - "data augmentation of wearable sensor data for parkinson s disease monitoring using convolutional neural networks"
    - "exercise motion classification from large scale wearable sensor data using convolutional neural networks"
    - "co occurrence feature learning for skeleton based action recognition using regularized deep lstm networks"

Connected through hyperedge HE-2 (induced by source paper "music oriented dance video synthesis with pose perceptual loss"):
  with members:
    - "dance dance generation motion transfer for internet videos"
    - "liquid warping gan a unified framework for human motion imitation appearance transfer and novel view synthesis"
    - "video to video synthesis"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "listen to dance music driven choreography generation using autoregressive encoder decoder network"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "deep video generation prediction and completion of human action sequences"
    - "co occurrence feature learning from skeleton data for action recognition and detection with hierarchical aggregation"

Connected through hyperedge HE-3 (induced by source paper "human motion prediction via spatio temporal inpainting"):
  with members:
    - "a neural temporal model for human motion prediction"
    - "convolutional sequence to sequence model for human dynamics"
    - "a style based generator architecture for generative adversarial networks"
    - "a neural network approach to missing marker reconstruction in human motion capture"
    - "which training methods for gans do actually converge"
    - "generative image inpainting with contextual attention"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "hp gan probabilistic 3d human motion prediction via gan"

Connected through hyperedge HE-4 (induced by source paper "learning and tracking the 3d body shape of freely moving infants from rgb d sequences"):
  with members:
    - "learning to estimate 3d human pose and shape from a single color image"
    - "video based reconstruction of 3d people models"
    - "neural body fitting unifying deep learning and model based human pose and shape estimation"
    - "detailed human avatars from monocular video"
    - "doublefusion real time capture of human performances with inner body shapes from a single depth sensor"
    - "detailed accurate human shape estimation from clothed 3d scan sequences"
    - "building statistical shape spaces for 3d human modeling"
    - "learning from synthetic humans"

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)