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: "learning to reconstruct 3d human pose and shape via model fitting in the loop"
  Abstract: Model-based human pose estimation is currently approached through two different paradigms. Optimization-based methods fit a parametric body model to 2D observations in an iterative manner, leading to accurate image-model alignments, but are often slow and sensitive to the initialization. In contrast, regression-based methods, that use a deep network to directly estimate the model parameters from pixels, tend to provide reasonable, but not pixel accurate, results while requiring huge amounts of supervision. In this work, instead of investigating which approach is better, our key insight is that the two paradigms can form a strong collaboration. A reasonable, directly regressed estimate from the network can initialize the iterative optimization making the fitting faster and more accurate. Similarly, a pixel accurate fit from iterative optimization can act as strong supervision for the network. This is the core of our proposed approach SPIN (SMPL oPtimization IN the loop). The deep network initializes an iterative optimization routine that fits the body model to 2D joints within the training loop, and the fitted estimate is subsequently used to supervise the network. Our approach is self-improving by nature, since better network estimates can lead the optimization to better solutions, while more accurate optimization fits provide better supervision for the network. We demonstrate the effectiveness of our approach in different settings, where 3D ground truth is scarce, or not available, and we consistently outperform the state-of-the-art model-based pose estimation approaches by significant margins. The project website with videos, results, and code can be found at this https URL.

Connected through hyperedge HE-1 (induced by source paper "3d dynamic scene graphs actionable spatial perception with places objects and humans"):
  with members:
    - "teaser fast and certifiable point cloud registration"
    - "semantickitti a dataset for semantic scene understanding of lidar sequences"
    - "panoptic segmentation"
    - "in perfect shape certifiably optimal 3d shape reconstruction from 2d landmarks"
    - "semi supervised semantic mapping through label propagation with semantic texture meshes"
    - "kimera an open source library for real time metric semantic localization and mapping"
    - "3 d scene graph a sparse and semantic representation of physical environments for intelligent agents"
    - "value iteration networks on multiple levels of abstraction"

Connected through hyperedge HE-2 (induced by source paper "implicit functions in feature space for 3d shape reconstruction and completion"):
  with members:
    - "the virtual tailor predicting clothing in 3d as a function of human pose shape and garment style"
    - "tex2shape detailed full human body geometry from a single image"
    - "geometric disentanglement for generative latent shape models"
    - "pointflow 3d point cloud generation with continuous normalizing flows"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "facsimile fast and accurate scans from an image in less than a second"
    - "learning to reconstruct people in clothing from a single rgb camera"
    - "360 degree textures of people in clothing from a single image"

Connected through hyperedge HE-3 (induced by source paper "posenet3d unsupervised 3d human shape and pose estimation"):
  with members:
    - "monocular 3d human pose estimation by generation and ordinal ranking"
    - "cross view fusion for 3d human pose estimation"
    - "learnable triangulation of human pose"
    - "distill knowledge from nrsfm for weakly supervised 3d pose learning"
    - "repnet weakly supervised training of an adversarial reprojection network for 3d human pose estimation"
    - "billion scale semi supervised learning for image classification"
    - "self supervised learning of 3d human pose using multi view geometry"
    - "texturepose supervising human mesh estimation with texture consistency"

Connected through hyperedge HE-4 (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"
    - "exploiting temporal context for 3d human pose estimation in the wild"

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"
    - "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"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "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 "hemlets posh learning part centric heatmap triplets for 3d human pose and shape 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"
    - "texturepose supervising human mesh estimation with texture consistency"
    - "denserac joint 3d pose and shape estimation by dense render and compare"
    - "hemlets pose learning part centric heatmap triplets for accurate 3d human pose estimation"
    - "convolutional mesh regression for single image human shape reconstruction"
    - "densebody directly regressing dense 3d human pose and shape from a single color image"
    - "ordinal depth supervision for 3d human pose estimation"

Connected through hyperedge HE-7 (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"
    - "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"
    - "vibe video inference for human body pose and shape estimation"

Connected through hyperedge HE-8 (induced by source paper "technical report reactive semantic planning in unexplored semantic environments using deep perceptual feedback"):
  with members:
    - "reactive navigation in partially familiar planar environments using semantic perceptual feedback"
    - "neural autonomous navigation with riemannian motion policy"
    - "yolov3 an incremental improvement"
    - "semi parametric topological memory for navigation"
    - "6 dof object pose from semantic keypoints"
    - "high speed flight in an ergodic forest"

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)