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: "exploring context with deep structured models for semantic segmentation"
  Abstract: State-of-the-art semantic image segmentation methods are mostly based on training deep convolutional neural networks (CNNs). In this work, we proffer to improve semantic segmentation with the use of contextual information. In particular, we explore `patch-patch' context and `patch-background' context in deep CNNs. We formulate deep structured models by combining CNNs and Conditional Random Fields (CRFs) for learning the patch-patch context between image regions. Specifically, we formulate CNN-based pairwise potential functions to capture semantic correlations between neighboring patches. Efficient piecewise training of the proposed deep structured model is then applied in order to avoid repeated expensive CRF inference during the course of back propagation. For capturing the patch-background context, we show that a network design with traditional multi-scale image inputs and sliding pyramid pooling is very effective for improving performance. We perform comprehensive evaluation of the proposed method. We achieve new state-of-the-art performance on a number of challenging semantic segmentation datasets including $NYUDv2$, $PASCAL$-$VOC2012$, $Cityscapes$, $PASCAL$-$Context$, $SUN$-$RGBD$, $SIFT$-$flow$, and $KITTI$ datasets. Particularly, we report an intersection-over-union score of $77.8$ on the $PASCAL$-$VOC2012$ dataset.

Connected through hyperedge HE-1 (induced by source paper "3dcfs fast and robust joint 3d semantic instance segmentation via coupled feature selection"):
  with members:
    - "a cnn annularly convolutional neural networks on point clouds"
    - "associatively segmenting instances and semantics in point clouds"
    - "dfanet deep feature aggregation for real time semantic segmentation"
    - "3d instance segmentation via multi task metric learning"
    - "pointconv deep convolutional networks on 3d point clouds"
    - "dynamic graph cnn for learning on point clouds"
    - "gspn generative shape proposal network for 3d instance segmentation in point cloud"
    - "sgpn similarity group proposal network for 3d point cloud instance segmentation"

Connected through hyperedge HE-2 (induced by source paper "structured knowledge distillation for dense prediction"):
  with members:
    - "enforcing geometric constraints of virtual normal for depth prediction"
    - "fastdepth fast monocular depth estimation on embedded systems"
    - "fcos fully convolutional one stage object detection"
    - "cornernet detecting objects as paired keypoints"
    - "espnet efficient spatial pyramid of dilated convolutions for semantic segmentation"
    - "deep attention based classification network for robust depth prediction"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "ocnet object context network for scene parsing"

Connected through hyperedge HE-3 (induced by source paper "video object segmentation and tracking a survey"):
  with members:
    - "interactive video object segmentation in the wild"
    - "fast and accurate online video object segmentation via tracking parts"
    - "instance embedding transfer to unsupervised video object segmentation"
    - "unsupervised video object segmentation using motion saliency guided spatio temporal propagation"
    - "cnn in mrf video object segmentation via inference in a cnn based higher order spatio temporal mrf"
    - "maskrnn instance level video object segmentation"
    - "videomatch matching based video object segmentation"
    - "unsupervised video object segmentation for deep reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "esnet an efficient symmetric network for real time semantic segmentation"):
  with members:
    - "espnet efficient spatial pyramid of dilated convolutions for semantic segmentation"
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "cgnet a light weight context guided network for semantic segmentation"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "large kernel matters improve semantic segmentation by global convolutional network"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "not all pixels are equal difficulty aware semantic segmentation via deep layer cascade"
    - "icnet for real time semantic segmentation on high resolution images"

Connected through hyperedge HE-5 (induced by source paper "multi modal attention based fusion model for semantic segmentation of rgb depth images"):
  with members:
    - "depth aware cnn for rgb d segmentation"
    - "depth adaptive deep neural network for semantic segmentation"
    - "pixel wise attentional gating for parsimonious pixel labeling"
    - "cbam convolutional block attention module"
    - "squeeze and excitation networks"
    - "joint 2d 3d semantic data for indoor scene understanding"
    - "lstm cf unifying context modeling and fusion with lstms for rgb d scene labeling"
    - "identity mappings in deep residual networks"

Connected through hyperedge HE-6 (induced by source paper "associatively segmenting instances and semantics in point clouds"):
  with members:
    - "panoptic segmentation"
    - "dynamic graph cnn for learning on point clouds"
    - "recurrent slice networks for 3d segmentation on point clouds"
    - "sgpn similarity group proposal network for 3d point cloud instance segmentation"
    - "large scale point cloud semantic segmentation with superpoint graphs"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "segcloud semantic segmentation of 3d point clouds"
    - "exploring spatial context for 3d semantic segmentation of point clouds"

Connected through hyperedge HE-7 (induced by source paper "on the importance of visual context for data augmentation in scene understanding"):
  with members:
    - "rendering synthetic objects into legacy photographs"
    - "weakly supervised instance segmentation using class peak response"
    - "semantic foggy scene understanding with synthetic data"
    - "modeling visual context is key to augmenting object detection datasets"
    - "synthetic data augmentation using gan for improved liver lesion classification"
    - "improved part segmentation performance by optimising realism of synthetic images using cycle generative adversarial networks"
    - "random erasing data augmentation"
    - "focal loss for dense object detection"

Connected through hyperedge HE-8 (induced by source paper "modanet a large scale street fashion dataset with polygon annotations"):
  with members:
    - "efficient interactive annotation of segmentation datasets with polygon rnn"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "soft nms improving object detection with one line of code"
    - "focal loss for dense object detection"
    - "deformable convolutional networks"
    - "annotating object instances with a polygon rnn"
    - "imagenet pre trained models with batch normalization"
    - "inception v4 inception resnet and the impact of residual connections on learning"

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)