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: "feature boosting network for 3d pose estimation"
  Abstract: In this paper, a feature boosting network is proposed for estimating 3D hand pose and 3D body pose from a single RGB image. In this method, the features learned by the convolutional layers are boosted with a new long short-term dependence-aware (LSTD) module, which enables the intermediate convolutional feature maps to perceive the graphical long short-term dependency among different hand (or body) parts using the designed Graphical ConvLSTM. Learning a set of features that are reliable and discriminatively representative of the pose of a hand (or body) part is difficult due to the ambiguities, texture and illumination variation, and self-occlusion in the real application of 3D pose estimation. To improve the reliability of the features for representing each body part and enhance the LSTD module, we further introduce a context consistency gate (CCG) in this paper, with which the convolutional feature maps are modulated according to their consistency with the context representations. We evaluate the proposed method on challenging benchmark datasets for 3D hand pose estimation and 3D full body pose estimation. Experimental results show the effectiveness of our method that achieves state-of-the-art performance on both of the tasks.

Connected through hyperedge HE-1 (induced by source paper "bi directional dermoscopic feature learning and multi scale consistent decision fusion for skin lesion segmentation"):
  with members:
    - "supervised saliency map driven segmentation of lesions in dermoscopic images"
    - "ce net context encoder network for 2d medical image segmentation"
    - "semantic correlation promoted shape variant context for segmentation"
    - "et net a generic edge attention guidance network for medical image segmentation"
    - "improving dermoscopic image segmentation with enhanced convolutional deconvolutional networks"
    - "boundary aware feature propagation for scene segmentation"
    - "attention gated networks learning to leverage salient regions in medical images"
    - "interactive medical image segmentation using deep learning with image specific fine tuning"

Connected through hyperedge HE-2 (induced by source paper "object 6d pose estimation with non local attention"):
  with members:
    - "semantic correlation promoted shape variant context for segmentation"
    - "boundary aware feature propagation for scene segmentation"
    - "non local neural networks"
    - "bb8 a scalable accurate robust to partial occlusion method for predicting the 3d poses of challenging objects without using depth"
    - "posecnn a convolutional neural network for 6d object pose estimation in cluttered scenes"
    - "3d bounding box estimation using deep learning and geometry"
    - "deep learning of local rgb d patches for 3d object detection and 6d pose estimation"
    - "ssd single shot multibox detector"

Connected through hyperedge HE-3 (induced by source paper "semantic correlation promoted shape variant context for segmentation"):
  with members:
    - "aanet attribute attention network for person re identifications"
    - "scene graph generation with external knowledge and image reconstruction"
    - "towards robust curve text detection with conditional spatial expansion"
    - "learning a discriminative feature network for semantic segmentation"
    - "adaptive affinity fields for semantic segmentation"
    - "a network structure to explicitly reduce confusion errors in semantic segmentation"
    - "a classification refinement strategy for semantic segmentation"
    - "learning markov clustering networks for scene text detection"

Connected through hyperedge HE-4 (induced by source paper "boundary aware feature propagation for scene segmentation"):
  with members:
    - "unpaired image captioning via scene graph alignments"
    - "scene graph generation with external knowledge and image reconstruction"
    - "adaptive context network for scene parsing"
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "learning a discriminative feature network for semantic segmentation"
    - "adaptive affinity fields for semantic segmentation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "unpaired image captioning by language pivoting"

Connected through hyperedge HE-5 (induced by source paper "ntu rgb d 120 a large scale benchmark for 3d human activity understanding"):
  with members:
    - "2d 3d pose estimation and action recognition using multitask deep learning"
    - "glimpse clouds human activity recognition from unstructured feature points"
    - "video representation learning using discriminative pooling"
    - "learning and refining of privileged information based rnns for action recognition from depth sequences"
    - "sphereface deep hypersphere embedding for face recognition"
    - "when kernel methods meet feature learning log covariance network for action recognition from skeletal data"
    - "modeling temporal dynamics and spatial configurations of actions using two stream recurrent neural networks"
    - "pose conditioned spatio temporal attention for human action recognition"

Connected through hyperedge HE-6 (induced by source paper "weakly supervised discovery of geometry aware representation for 3d human pose estimation"):
  with members:
    - "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"
    - "deep autoencoder for combined human pose estimation and body model upscaling"

Connected through hyperedge HE-7 (induced by source paper "fetusmap fetal pose estimation in 3d ultrasound"):
  with members:
    - "less is more simultaneous view classification and landmark detection for abdominal ultrasound images"
    - "interactive medical image segmentation using deep learning with image specific fine tuning"
    - "adversarial posenet a structure aware convolutional network for human pose estimation"
    - "sononet real time detection and localisation of fetal standard scan planes in freehand ultrasound"
    - "training deep nets with sublinear memory cost"
    - "u net convolutional networks for biomedical image segmentation"

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)