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: "openpose realtime multi person 2d pose estimation using part affinity fields"
  Abstract: Realtime multi-person 2D pose estimation is a key component in enabling machines to have an understanding of people in images and videos. In this work, we present a realtime approach to detect the 2D pose of multiple people in an image. The proposed method uses a nonparametric representation, which we refer to as Part Affinity Fields (PAFs), to learn to associate body parts with individuals in the image. This bottom-up system achieves high accuracy and realtime performance, regardless of the number of people in the image. In previous work, PAFs and body part location estimation were refined simultaneously across training stages. We demonstrate that a PAF-only refinement rather than both PAF and body part location refinement results in a substantial increase in both runtime performance and accuracy. We also present the first combined body and foot keypoint detector, based on an internal annotated foot dataset that we have publicly released. We show that the combined detector not only reduces the inference time compared to running them sequentially, but also maintains the accuracy of each component individually. This work has culminated in the release of OpenPose, the first open-source realtime system for multi-person 2D pose detection, including body, foot, hand, and facial keypoints.

Connected through hyperedge HE-1 (induced by source paper "the state of service robots current bottlenecks in object perception and manipulation"):
  with members:
    - "learning to singulate objects using a push proposal network"
    - "investigating the importance of shape features color constancy color spaces and similarity measures in open ended 3d object recognition"
    - "accelerating reinforcement learning for reaching using continuous curriculum learning"
    - "4d spatio temporal convnets minkowski convolutional neural networks"
    - "learning better generative models for dexterous single view grasping of novel objects"
    - "6 dof grasping for target driven object manipulation in clutter"
    - "the sound of motions"
    - "co separating sounds of visual objects"

Connected through hyperedge HE-2 (induced by source paper "spatiotemporal relationship reasoning for pedestrian intent prediction"):
  with members:
    - "action anticipation with rbf kernelized feature mapping rnn"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "disentangling human dynamics for pedestrian locomotion forecasting with noisy supervision"
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "is the pedestrian going to cross answering by 2d pose estimation"
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "scene graph generation via conditional random fields"
    - "a transferable pedestrian motion prediction model for intersections with different geometries"

Connected through hyperedge HE-3 (induced by source paper "a survey on 3d skeleton based action recognition using learning method"):
  with members:
    - "semantics guided neural networks for efficient skeleton based human action recognition"
    - "richlt activated graph convolutional network for action recognition with incomplete skeletons"
    - "make skeleton based action recognition model smaller faster and better"
    - "an attention enhanced graph convolutional lstm network for skeleton based action recognition"
    - "ntu rgb d 120 a large scale benchmark for 3d human activity understanding"
    - "skeleton image representation for 3d action recognition based on tree structure and reference joints"
    - "a comparative review of recent kinect based action recognition algorithms"
    - "skelemotion a new representation of skeleton joint sequences based on motion information for 3d action recognition"

Connected through hyperedge HE-4 (induced by source paper "neural human video rendering joint learning of dynamic textures and rendering to video translation"):
  with members:
    - "deferred neural rendering image synthesis using neural textures"
    - "textured neural avatars"
    - "neural rendering and reenactment of human actor videos"
    - "liquid warping gan a unified framework for human motion imitation appearance transfer and novel view synthesis"
    - "dense intrinsic appearance flow for human pose transfer"
    - "video to video synthesis"
    - "synthesizing images of humans in unseen poses"
    - "learning to estimate 3d human pose and shape from a single color image"

Connected through hyperedge HE-5 (induced by source paper "movi a large multipurpose motion and video dataset"):
  with members:
    - "amass archive of motion capture as surface shapes"
    - "learning to reconstruct 3d human pose and shape via model fitting in the loop"
    - "expressive body capture 3d hands face and body from a single image"
    - "deep high resolution representation learning for human pose estimation"
    - "modeling human motion with quaternion based neural networks"
    - "quaternet a quaternion based recurrent model for human motion"
    - "learning to estimate 3d human pose and shape from a single color image"
    - "3d human pose estimation in video with temporal convolutions and semi supervised training"

Connected through hyperedge HE-6 (induced by source paper "hierarchical kinematic human mesh recovery"):
  with members:
    - "repose learning deep kinematic priors for fast human pose estimation"
    - "texturepose supervising human mesh estimation with texture consistency"
    - "denserac joint 3d pose and shape estimation by dense render and compare"
    - "learning to reconstruct 3d human pose and shape via model fitting in the loop"
    - "convolutional mesh regression for single image human shape reconstruction"
    - "expressive body capture 3d hands face and body from a single image"
    - "exploiting temporal context for 3d human pose estimation in the wild"
    - "densebody directly regressing dense 3d human pose and shape from a single color image"

Connected through hyperedge HE-7 (induced by source paper "predictively encoded graph convolutional network for noise robust skeleton based action recognition"):
  with members:
    - "actional structural graph convolutional networks for skeleton based action recognition"
    - "driver drowsiness detection using condition adaptive representation learning framework"
    - "richlt activated graph convolutional network for action recognition with incomplete skeletons"
    - "learning graph convolutional network for skeleton based human action recognition by neural searching"
    - "an attention enhanced graph convolutional lstm network for skeleton based action recognition"
    - "skeleton based action recognition with multi stream adaptive graph convolutional networks"
    - "hallucinating idt descriptors and i3d optical flow features for action recognition with cnns"
    - "densefusion 6d object pose estimation by iterative dense fusion"

Connected through hyperedge HE-8 (induced by source paper "deepcap monocular human performance capture using weak supervision"):
  with members:
    - "tailornet predicting clothing in 3d as a function of human pose shape and garment style"
    - "implicit functions in feature space for 3d shape reconstruction and completion"
    - "tex2shape detailed full human body geometry from a single image"
    - "learning to dress 3d people in generative clothing"
    - "learning to reconstruct people in clothing from a single rgb camera"
    - "deephuman 3d human reconstruction from a single image"
    - "multi garment net learning to dress 3d people from images"
    - "moulding humans non parametric 3d human shape estimation from single images"

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)