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 a deep convnet for multi label classification with partial labels"
  Abstract: Deep ConvNets have shown great performance for single-label image classification (e.g. ImageNet), but it is necessary to move beyond the single-label classification task because pictures of everyday life are inherently multi-label. Multi-label classification is a more difficult task than single-label classification because both the input images and output label spaces are more complex. Furthermore, collecting clean multi-label annotations is more difficult to scale-up than single-label annotations. To reduce the annotation cost, we propose to train a model with partial labels i.e. only some labels are known per image. We first empirically compare different labeling strategies to show the potential for using partial labels on multi-label datasets. Then to learn with partial labels, we introduce a new classification loss that exploits the proportion of known labels per example. Our approach allows the use of the same training settings as when learning with all the annotations. We further explore several curriculum learning based strategies to predict missing labels. Experiments are performed on three large-scale multi-label datasets: MS COCO, NUS-WIDE and Open Images.

Connected through hyperedge HE-1 (induced by source paper "multi organ segmentation over partially labeled datasets with multi scale feature abstraction"):
  with members:
    - "boundary weighted domain adaptive neural network for prostate mr image segmentation"
    - "use net incorporating squeeze and excitation blocks into u net for prostate zonal segmentation of multi institutional mri datasets"
    - "feature fusion encoder decoder network for automatic liver lesion segmentation"
    - "ce net context encoder network for 2d medical image segmentation"
    - "the liver tumor segmentation benchmark lits"
    - "abdominal multi organ segmentation with organ attention networks and statistical fusion"
    - "training of convolutional networks on multiple heterogeneous datasets for street scene semantic segmentation"
    - "joint optic disc and cup segmentation based on multi label deep network and polar transformation"

Connected through hyperedge HE-2 (induced by source paper "dynamic graph correlation learning for disease diagnosis with incomplete labels"):
  with members:
    - "imagegcn multi relational image graph convolutional networks for disease identification with chest x rays"
    - "multi label image recognition with graph convolutional networks"
    - "semi supervised multi organ segmentation via deep multi planar co training"
    - "attention guided curriculum learning for weakly supervised classification and localization of thoracic diseases on chest radiographs"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "learning to diagnose from scratch by exploiting dependencies among labels"
    - "exploring the limits of weakly supervised pretraining"
    - "diagnose like a radiologist attention guided convolutional neural network for thorax disease classification"

Connected through hyperedge HE-3 (induced by source paper "learning an efficient network for large scale hierarchical object detection with data imbalance 3rd place solution to open images challenge 2019"):
  with members:
    - "large scale interactive object segmentation with human annotators"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "learning data augmentation strategies for object detection"
    - "rethinking imagenet pre training"
    - "sampling techniques for large scale object detection from sparsely annotated objects"
    - "yolov3 an incremental improvement"
    - "pfdet 2nd place solution to open images challenge 2018 object detection track"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"

Connected through hyperedge HE-4 (induced by source paper "an attention mechanism for musical instrument recognition"):
  with members:
    - "frame level instrument recognition by timbre and pitch"
    - "multitask learning for frame level instrument recognition"
    - "deep sets"
    - "extended playing techniques the next milestone in musical instrument recognition"
    - "adaptive pooling operators for weakly labeled sound event detection"
    - "convolutional recurrent neural networks for polyphonic sound event detection"
    - "attention is all you need"
    - "sound event detection using spatial features and convolutional recurrent neural network"

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)