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: "deep autoencoder for combined human pose estimation and body model upscaling"
  Abstract: We present a method for simultaneously estimating 3D human pose and body shape from a sparse set of wide-baseline camera views. We train a symmetric convolutional autoencoder with a dual loss that enforces learning of a latent representation that encodes skeletal joint positions, and at the same time learns a deep representation of volumetric body shape. We harness the latter to up-scale input volumetric data by a factor of $4 \times$, whilst recovering a 3D estimate of joint positions with equal or greater accuracy than the state of the art. Inference runs in real-time (25 fps) and has the potential for passive human behaviour monitoring where there is a requirement for high fidelity estimation of human body shape and pose.

Connected through hyperedge HE-1 (induced by source paper "adversarial based neural networks for affect estimations in the wild"):
  with members:
    - "expression affect action unit recognition aff wild2 multi task learning and arcface"
    - "avec 2019 workshop and challenge state of mind detecting depression with ai and cross cultural affect recognition"
    - "end to end facial and physiological model for affective computing and applications"
    - "sewa db a rich database for audio visual emotion and sentiment research in the wild"
    - "a multi task learning generation framework valence arousal action units primary expressions"
    - "aff wild2 extending the aff wild database for affect recognition"
    - "adversarial training in affective computing and sentiment analysis recent advances and perspectives"
    - "semi supervised learning with gans manifold invariance with improved inference"

Connected through hyperedge HE-2 (induced by source paper "weakly supervised discovery of geometry aware representation for 3d human pose estimation"):
  with members:
    - "feature boosting network for 3d pose estimation"
    - "ordinal depth supervision for 3d human pose estimation"
    - "discovery of latent 3d keypoints via end to end geometric reasoning"
    - "learning pose specific representations by predicting different views"
    - "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"
    - "deepmvs learning multi view stereopsis"

Connected through hyperedge HE-3 (induced by source paper "deepfuse an imu aware network for real time 3d human pose estimation from multi view image"):
  with members:
    - "self supervised learning of 3d human pose using multi view geometry"
    - "fast and robust multi person 3d pose estimation from multiple views"
    - "rethinking pose in 3d multi stage refinement and recovery for markerless motion capture"
    - "a generalizable approach for multi view 3d human pose regression"
    - "multi scale structure aware network for human pose estimation"
    - "2d 3d pose estimation and action recognition using multitask deep learning"
    - "3d human pose estimation with 2d marginal heatmaps"
    - "numerical coordinate regression with convolutional neural networks"

Connected through hyperedge HE-4 (induced by source paper "disentangling human dynamics for pedestrian locomotion forecasting with noisy supervision"):
  with members:
    - "uncertainty aware driver trajectory prediction at urban intersections"
    - "what the constant velocity model can teach us about pedestrian motion prediction"
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "egocentric vision based future vehicle localization for intelligent driving assistance systems"
    - "convolutional sequence to sequence model for human dynamics"
    - "action agnostic human pose forecasting"
    - "bdd100k a diverse driving video database with scalable annotation tooling"

Connected through hyperedge HE-5 (induced by source paper "shape aware human pose and shape reconstruction using multi view images"):
  with members:
    - "learning to reconstruct people in clothing from a single rgb camera"
    - "convolutional mesh regression for single image human shape reconstruction"
    - "rethinking pose in 3d multi stage refinement and recovery for markerless motion capture"
    - "learning to estimate 3d human pose and shape from a single color image"
    - "unsupervised geometry aware representation for 3d human pose estimation"
    - "bodynet volumetric inference of 3d human body shapes"
    - "fashion is taking shape understanding clothing preference based on body shape from online sources"
    - "neural body fitting unifying deep learning and model based human pose and shape estimation"

Connected through hyperedge HE-6 (induced by source paper "consensus based optimization for 3d human pose estimation in camera coordinates"):
  with members:
    - "self supervised learning of 3d human pose using multi view geometry"
    - "how do neural networks see depth in single images"
    - "2d 3d pose estimation and action recognition using multitask deep learning"
    - "3d human pose estimation in the wild by adversarial learning"
    - "fbi pose towards bridging the gap between 2d images and 3d human poses using forward or backward information"
    - "exploiting temporal information for 3d human pose estimation"
    - "learning monocular 3d human pose estimation from multi view images"
    - "towards 3d human pose estimation in the wild a weakly supervised approach"

Connected through hyperedge HE-7 (induced by source paper "learning to reconstruct people in clothing from a single rgb camera"):
  with members:
    - "generating 3d faces using convolutional mesh autoencoders"
    - "learning to estimate 3d human pose and shape from a single color image"
    - "bodynet volumetric inference of 3d human body shapes"
    - "instance level human parsing via part grouping network"
    - "total capture a 3d deformation model for tracking faces hands and bodies"
    - "video based reconstruction of 3d people models"
    - "neural body fitting unifying deep learning and model based human pose and shape estimation"
    - "learning to generate and reconstruct 3d meshes with only 2d supervision"

Connected through hyperedge HE-8 (induced by source paper "semantic estimation of 3d body shape and pose using minimal cameras"):
  with members:
    - "volumetric performance capture from minimal camera viewpoints"
    - "exploiting temporal information for 3d human pose estimation"
    - "volumetric super resolution of multispectral data"
    - "lifting from the deep convolutional 3d pose estimation from a single image"
    - "harvesting multiple views for marker less 3d human pose annotations"
    - "a simple yet effective baseline for 3d human pose estimation"
    - "recurrent 3d pose sequence machines"
    - "convolutional pose machines"

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)