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: "end to end instance segmentation with recurrent attention"
  Abstract: While convolutional neural networks have gained impressive success recently in solving structured prediction problems such as semantic segmentation, it remains a challenge to differentiate individual object instances in the scene. Instance segmentation is very important in a variety of applications, such as autonomous driving, image captioning, and visual question answering. Techniques that combine large graphical models with low-level vision have been proposed to address this problem; however, we propose an end-to-end recurrent neural network (RNN) architecture with an attention mechanism to model a human-like counting process, and produce detailed instance segmentations. The network is jointly trained to sequentially produce regions of interest as well as a dominant object segmentation within each region. The proposed model achieves competitive results on the CVPPP, KITTI, and Cityscapes datasets.

Connected through hyperedge HE-1 (induced by source paper "deep multicameral decoding for localizing unoccluded object instances from a single rgb image"):
  with members:
    - "fully dense unet for 2 d sparse photoacoustic tomography artifact removal"
    - "dual graph convolutional network for semantic segmentation"
    - "pointrend image segmentation as rendering"
    - "quantized densely connected u nets for efficient landmark localization"
    - "object segmentation in depth maps with one user click and a synthetically trained fully convolutional network"
    - "mvtec d2s densely segmented supermarket dataset"
    - "deep ordinal regression network for monocular depth estimation"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"

Connected through hyperedge HE-2 (induced by source paper "bi directional attention for joint instance and semantic segmentation in point clouds"):
  with members:
    - "learning object bounding boxes for 3d instance segmentation on point clouds"
    - "3d bird s eye view instance segmentation"
    - "jsnet joint instance and semantic segmentation of 3d point clouds"
    - "associatively segmenting instances and semantics in point clouds"
    - "partnet a large scale benchmark for fine grained and hierarchical part level 3d object understanding"
    - "pointconv deep convolutional networks on 3d point clouds"
    - "fully convolutional point networks for large scale point clouds"
    - "dynamic graph cnn for learning on point clouds"

Connected through hyperedge HE-3 (induced by source paper "cell r cnn v3 a novel panoptic paradigm for instance segmentation in biomedical images"):
  with members:
    - "panoptic segmentation"
    - "an end to end network for panoptic segmentation"
    - "data augmentation for leaf segmentation and counting tasks in rosette plants"
    - "mask scoring r cnn"
    - "instance segmentation of biological images using harmonic embeddings"
    - "panoptic feature pyramid networks"
    - "cascade r cnn high quality object detection and instance segmentation"
    - "deep leaf segmentation using synthetic data"

Connected through hyperedge HE-4 (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-5 (induced by source paper "deeperlab single shot image parser"):
  with members:
    - "panoptic segmentation"
    - "cornernet detecting objects as paired keypoints"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "personlab person pose estimation and instance segmentation with a bottom up part based geometric embedding model"
    - "affinity derivation and graph merge for instance segmentation"
    - "unified perceptual parsing for scene understanding"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "path aggregation network for instance segmentation"

Connected through hyperedge HE-6 (induced by source paper "spatial sampling network for fast scene understanding"):
  with members:
    - "espnet efficient spatial pyramid of dilated convolutions for semantic segmentation"
    - "benchmark analysis of representative deep neural network architectures"
    - "guided upsampling network for real time semantic segmentation"
    - "contextnet exploring context and detail for semantic segmentation in real time"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "path aggregation network for instance segmentation"
    - "efficient dense modules of asymmetric convolution for real time semantic segmentation"
    - "multi task learning using uncertainty to weigh losses for scene geometry and semantics"

Connected through hyperedge HE-7 (induced by source paper "ssap single shot instance segmentation with affinity pyramid"):
  with members:
    - "panoptic segmentation"
    - "deeperlab single shot image parser"
    - "instance segmentation by jointly optimizing spatial embeddings and clustering bandwidth"
    - "learning pixel level semantic affinity with image level supervision for weakly supervised semantic segmentation"
    - "affinity derivation and graph merge for instance segmentation"
    - "adaptive affinity fields for semantic segmentation"
    - "path aggregation network for instance segmentation"
    - "weakly and semi supervised panoptic segmentation"

Connected through hyperedge HE-8 (induced by source paper "instance segmentation with point supervision"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "retinamask learning to predict masks improves state of the art single shot detection for free"
    - "object counting and instance segmentation with image level supervision"
    - "weakly supervised instance segmentation using class peak response"
    - "learning to segment via cut and paste"
    - "where are the blobs counting by localization with point supervision"
    - "learning pixel level semantic affinity with image level supervision for weakly supervised semantic segmentation"
    - "on the importance of visual context for data augmentation in scene understanding"

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.LG(Machine Learning)