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: "instance segmentation by deep coloring"
  Abstract: We propose a new and, arguably, a very simple reduction of instance segmentation to semantic segmentation. This reduction allows to train feed-forward non-recurrent deep instance segmentation systems in an end-to-end fashion using architectures that have been proposed for semantic segmentation. Our approach proceeds by introducing a fixed number of labels (colors) and then dynamically assigning object instances to those labels during training (coloring). A standard semantic segmentation objective is then used to train a network that can color previously unseen images. At test time, individual object instances can be recovered from the output of the trained convolutional network using simple connected component analysis. In the experimental validation, the coloring approach is shown to be capable of solving diverse instance segmentation tasks arising in autonomous driving (the Cityscapes benchmark), plant phenotyping (the CVPPP leaf segmentation challenge), and high-throughput microscopy image analysis. #R##N#The source code is publicly available: this https URL.

Connected through hyperedge HE-1 (induced by source paper "deep affinity net instance segmentation via affinity"):
  with members:
    - "weakly supervised learning of instance segmentation with inter pixel relations"
    - "upsnet a unified panoptic segmentation network"
    - "deeperlab single shot image parser"
    - "instance segmentation by jointly optimizing spatial embeddings and clustering bandwidth"
    - "panoptic feature pyramid networks"
    - "cornernet detecting objects as paired keypoints"
    - "personlab person pose estimation and instance segmentation with a bottom up part based geometric embedding model"
    - "bshapenet object detection and instance segmentation with bounding shape masks"

Connected through hyperedge HE-2 (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-3 (induced by source paper "a generalized framework for agglomerative clustering of signed graphs applied to instance segmentation"):
  with members:
    - "the mutex watershed and its objective efficient parameter free image partitioning"
    - "affinity derivation and graph merge for instance segmentation"
    - "path aggregation network for instance segmentation"
    - "cell detection with star convex polygons"
    - "superhuman accuracy on the snemi3d connectomics challenge"
    - "anisotropic em segmentation by 3d affinity learning and agglomeration"
    - "recurrent pixel embedding for instance grouping"
    - "pixelwise instance segmentation with a dynamically instantiated network"

Connected through hyperedge HE-4 (induced by source paper "a hierarchical probabilistic u net for modeling multi scale ambiguities"):
  with members:
    - "panoptic segmentation"
    - "upsnet a unified panoptic segmentation network"
    - "hierarchical autoregressive image models with auxiliary decoders"
    - "tensormask a foundation for dense object segmentation"
    - "instance segmentation of biological images using harmonic embeddings"
    - "semantic image synthesis with spatially adaptive normalization"
    - "deep learning to achieve clinically applicable segmentation of head and neck anatomy for radiotherapy"
    - "generating high fidelity images with subscale pixel networks and multidimensional upscaling"

Connected through hyperedge HE-5 (induced by source paper "counting and segmenting sorghum heads"):
  with members:
    - "crowd counting with decomposed uncertainty"
    - "embedded cnn based vehicle classification and counting in non laned road traffic"
    - "adaptive scenario discovery for crowd counting"
    - "a ccnn adaptive ccnn for density estimation and crowd counting"
    - "maskrnn instance level video object segmentation"
    - "using deep learning for segmentation and counting within microscopy data"
    - "csrnet dilated convolutional neural networks for understanding the highly congested scenes"
    - "iterative crowd counting"

Connected through hyperedge HE-6 (induced by source paper "instance segmentation of biological images using harmonic embeddings"):
  with members:
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "recurrent pixel embedding for instance grouping"
    - "semantic instance segmentation via deep metric learning"
    - "semantic instance segmentation with a discriminative loss function"
    - "mask r cnn"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "instance sensitive fully convolutional networks"
    - "parsing images of overlapping organisms with deep singling out networks"

Connected through hyperedge HE-7 (induced by source paper "cross classification clustering an efficient multi object tracking technique for 3 d instance segmentation in connectomics"):
  with members:
    - "superhuman accuracy on the snemi3d connectomics challenge"
    - "anisotropic em segmentation by 3d affinity learning and agglomeration"
    - "morphological error detection in 3d segmentations"
    - "an error detection and correction framework for connectomics"
    - "detect and track efficient pose estimation in videos"
    - "large margin object tracking with circulant feature maps"
    - "object detection from video tubelets with convolutional neural networks"
    - "v net fully convolutional neural networks for volumetric medical 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)