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 multitask architecture for integrated 2d and 3d human sensing"
  Abstract: We propose a deep multitask architecture for \emph{fully automatic 2d and 3d human sensing} (DMHS), including \emph{recognition and reconstruction}, in \emph{monocular images}. The system computes the figure-ground segmentation, semantically identifies the human body parts at pixel level, and estimates the 2d and 3d pose of the person. The model supports the joint training of all components by means of multi-task losses where early processing stages recursively feed into advanced ones for increasingly complex calculations, accuracy and robustness. The design allows us to tie a complete training protocol, by taking advantage of multiple datasets that would otherwise restrictively cover only some of the model components: complex 2d image data with no body part labeling and without associated 3d ground truth, or complex 3d data with limited 2d background variability. In detailed experiments based on several challenging 2d and 3d datasets (LSP, HumanEva, Human3.6M), we evaluate the sub-structures of the model, the effect of various types of training data in the multitask loss, and demonstrate that state-of-the-art results can be achieved at all processing levels. We also show that in the wild our monocular RGB architecture is perceptually competitive to a state-of-the art (commercial) Kinect system based on RGB-D data.

Connected through hyperedge HE-1 (induced by source paper "weakly supervised 3d human pose learning via multi view images in the wild"):
  with members:
    - "distill knowledge from nrsfm for weakly supervised 3d pose learning"
    - "repnet weakly supervised training of an adversarial reprojection network for 3d human pose estimation"
    - "self supervised learning of 3d human pose using multi view geometry"
    - "texturepose supervising human mesh estimation with texture consistency"
    - "in the wild human pose estimation using explicit 2d features and intermediate 3d representations"
    - "learning the depths of moving people by watching frozen people"
    - "c3dpo canonical 3d pose networks for non rigid structure from motion"
    - "learning to reconstruct 3d human pose and shape via model fitting in the loop"

Connected through hyperedge HE-2 (induced by source paper "deformation aware unpaired image translation for pose estimation on laboratory animals"):
  with members:
    - "amass archive of motion capture as surface shapes"
    - "transgaga geometry aware unsupervised image to image translation"
    - "emergence of object segmentation in perturbed generative models"
    - "repnet weakly supervised training of an adversarial reprojection network for 3d human pose estimation"
    - "three d safari learning to estimate zebra pose shape and texture from images in the wild"
    - "learning conditional deformable templates with convolutional networks"
    - "understanding straight through estimator in training activation quantized neural nets"
    - "voxelmorph a learning framework for deformable medical image registration"

Connected through hyperedge HE-3 (induced by source paper "chained representation cycling learning to estimate 3d human pose and shape by cycling between representations"):
  with members:
    - "unsupervised part based disentangling of object shape and appearance"
    - "unsupervised learning of object structure and dynamics from videos"
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "ordinal depth supervision for 3d human pose estimation"
    - "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"
    - "bodynet volumetric inference of 3d human body shapes"

Connected through hyperedge HE-4 (induced by source paper "deep reinforcement learning for active human pose estimation"):
  with members:
    - "ordinal depth supervision for 3d human pose estimation"
    - "geometry aware recurrent neural networks for active visual recognition"
    - "total capture a 3d deformation model for tracking faces hands and bodies"
    - "gibson env real world perception for embodied agents"
    - "reinforcement learning of active vision for manipulating objects under occlusions"
    - "learning cooperative visual dialog agents with deep reinforcement learning"
    - "end to end recovery of human shape and pose"
    - "a dataset for developing and benchmarking active vision"

Connected through hyperedge HE-5 (induced by source paper "multi task deep learning for real time 3d human pose estimation and action recognition"):
  with members:
    - "deep high resolution representation learning for human pose estimation"
    - "2d 3d pose estimation and action recognition using multitask deep learning"
    - "3d human pose estimation in the wild by adversarial learning"
    - "glimpse clouds human activity recognition from unstructured feature points"
    - "hand pose estimation via latent 2 5d heatmap regression"
    - "monocap monocular human motion capture using a cnn coupled with a geometric prior"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "lifting from the deep convolutional 3d pose estimation from a single image"

Connected through hyperedge HE-6 (induced by source paper "xnect real time multi person 3d human pose estimation with a single rgb camera"):
  with members:
    - "learning to reconstruct people in clothing from a single rgb camera"
    - "expressive body capture 3d hands face and body from a single image"
    - "lcr net multi person 2d and 3d pose detection in natural images"
    - "ordinal depth supervision for 3d human pose estimation"
    - "espnet efficient spatial pyramid of dilated convolutions for semantic segmentation"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "learning to estimate 3d human pose and shape from a single color image"
    - "regularized evolution for image classifier architecture search"

Connected through hyperedge HE-7 (induced by source paper "cross domain complementary learning with synthetic data for multi person part segmentation"):
  with members:
    - "weakly and semi supervised human body part parsing via pose guided knowledge transfer"
    - "learning to adapt structured output space for semantic segmentation"
    - "training deep networks with synthetic data bridging the reality gap by domain randomization"
    - "densepose dense human pose estimation in the wild"
    - "look into person self supervised structure sensitive learning and a new benchmark for human parsing"
    - "towards 3d human pose estimation in the wild a weakly supervised approach"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "adversarial discriminative domain adaptation"

Connected through hyperedge HE-8 (induced by source paper "multiview consistent semi supervised learning for 3d human pose estimation"):
  with members:
    - "monocular 3d human pose estimation by generation and ordinal ranking"
    - "repnet weakly supervised training of an adversarial reprojection network for 3d human pose estimation"
    - "self supervised learning of 3d human pose using multi view geometry"
    - "in the wild human pose estimation using explicit 2d features and intermediate 3d representations"
    - "3d human pose machines with self supervised learning"
    - "learning to reconstruct 3d human pose and shape via model fitting in the loop"
    - "semantic graph convolutional networks for 3d human pose regression"
    - "exploiting temporal context for 3d human pose estimation in the wild"

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)