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: "numerical coordinate regression with convolutional neural networks"
  Abstract: We study deep learning approaches to inferring numerical coordinates for points of interest in an input image. Existing convolutional neural network-based solutions to this problem either take a heatmap matching approach or regress to coordinates with a fully connected output layer. Neither of these approaches is ideal, since the former is not entirely differentiable, and the latter lacks inherent spatial generalization. We propose our differentiable spatial to numerical transform (DSNT) to fill this gap. The DSNT layer adds no trainable parameters, is fully differentiable, and exhibits good spatial generalization. Unlike heatmap matching, DSNT works well with low heatmap resolutions, so it can be dropped in as an output layer for a wide range of existing fully convolutional architectures. Consequently, DSNT offers a better trade-off between inference speed and prediction accuracy compared to existing techniques. When used to replace the popular heatmap matching approach used in almost all state-of-the-art methods for pose estimation, DSNT gives better prediction accuracy for all model architectures tested.

Connected through hyperedge HE-1 (induced by source paper "metric scale truncation robust heatmaps for 3d human pose estimation"):
  with members:
    - "in the wild human pose estimation using explicit 2d features and intermediate 3d representations"
    - "ordinal depth supervision for 3d human pose estimation"
    - "multi scale structure aware network for human pose estimation"
    - "yolov3 an incremental improvement"
    - "video based reconstruction of 3d people models"
    - "2d 3d pose estimation and action recognition using multitask deep learning"
    - "how robust is 3d human pose estimation to occlusion"
    - "3d human pose estimation in the wild by adversarial learning"

Connected through hyperedge HE-2 (induced by source paper "head and tail localization of c elegans"):
  with members:
    - "stacked hourglass networks for human pose estimation"
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image recognition"

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"
    - "deep autoencoder for combined human pose estimation and body model upscaling"
    - "2d 3d pose estimation and action recognition using multitask deep learning"
    - "3d human pose estimation with 2d marginal heatmaps"

Connected through hyperedge HE-4 (induced by source paper "multitask learning to improve egocentric action recognition"):
  with members:
    - "mutual context network for jointly estimating egocentric gaze and actions"
    - "saliency tubes visual explanations for spatio temporal convolutions"
    - "egocentric hand track and object based human action recognition"
    - "what would you expect anticipating egocentric actions with rolling unrolling lstms and modality attention"
    - "long term feature banks for detailed video understanding"
    - "scaling egocentric vision the epic kitchens dataset"
    - "multi fiber networks for video recognition"
    - "yolov3 an incremental improvement"

Connected through hyperedge HE-5 (induced by source paper "end to end learnable geometric vision by backpropagating pnp optimization"):
  with members:
    - "neural guided ransac learning where to sample model hypotheses"
    - "understanding the limitations of cnn based absolute camera pose regression"
    - "satellite pose estimation with deep landmark regression and nonlinear pose refinement"
    - "expert sample consensus applied to camera re localization"
    - "deep high resolution representation learning for human pose estimation"
    - "discovery of latent 3d keypoints via end to end geometric reasoning"
    - "geometric loss functions for camera pose regression with deep learning"
    - "unsupervised learning of depth and ego motion from video"

Connected through hyperedge HE-6 (induced by source paper "absposelifter absolute 3d human pose lifting network from a single noisy 2d human pose"):
  with members:
    - "posefix model agnostic general human pose refinement network"
    - "simple baselines for human pose estimation and tracking"
    - "3d human pose estimation in the wild by adversarial learning"
    - "synthetic occlusion augmentation with volumetric heatmaps for the 2018 eccv posetrack challenge on 3d human pose estimation"
    - "monocap monocular human motion capture using a cnn coupled with a geometric prior"
    - "benchmarking and error diagnosis in multi instance pose estimation"
    - "learning pose grammar to encode human body configuration for 3d pose estimation"
    - "towards 3d human pose estimation in the wild a weakly supervised approach"

Connected through hyperedge HE-7 (induced by source paper "bpnp further empowering end to end learning with back propagatable geometric optimization"):
  with members:
    - "understanding the limitations of cnn based absolute camera pose regression"
    - "deep high resolution representation learning for human pose estimation"
    - "discovery of latent 3d keypoints via end to end geometric reasoning"
    - "geometric loss functions for camera pose regression with deep learning"
    - "unsupervised learning of depth and ego motion from video"
    - "geometry aware learning of maps for camera localization"
    - "optnet differentiable optimization as a layer in neural networks"
    - "bb8 a scalable accurate robust to partial occlusion method for predicting the 3d poses of challenging objects without using depth"

Connected through hyperedge HE-8 (induced by source paper "putting humans in a scene learning affordance in 3d indoor environments"):
  with members:
    - "neural inverse rendering of an indoor scene from a single image"
    - "binge watching scaling affordance learning from sitcoms"
    - "virtualhome simulating household activities via programs"
    - "st gan spatial transformer generative adversarial networks for image compositing"
    - "pedestrian synthesis gan generating pedestrian data in real scene and beyond"
    - "context aware synthesis and placement of object instances"
    - "unified perceptual parsing for scene understanding"
    - "learning to act properly predicting and explaining affordances from 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)