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: "shapemask learning to segment novel objects by refining shape priors"
  Abstract: Instance segmentation aims to detect and segment individual objects in a scene. Most existing methods rely on precise mask annotations of every category. However, it is difficult and costly to segment objects in novel categories because a large number of mask annotations is required. We introduce ShapeMask, which learns the intermediate concept of object shape to address the problem of generalization in instance segmentation to novel categories. ShapeMask starts with a bounding box detection and gradually refines it by first estimating the shape of the detected object through a collection of shape priors. Next, ShapeMask refines the coarse shape into an instance level mask by learning instance embeddings. The shape priors provide a strong cue for object-like prediction, and the instance embeddings model the instance specific appearance information. ShapeMask significantly outperforms the state-of-the-art by 6.4 and 3.8 AP when learning across categories, and obtains competitive performance in the fully supervised setting. It is also robust to inaccurate detections, decreased model capacity, and small training data. Moreover, it runs efficiently with 150ms inference time and trains within 11 hours on TPUs. With a larger backbone model, ShapeMask increases the gap with state-of-the-art to 9.4 and 6.2 AP across categories. Code will be released.

Connected through hyperedge HE-1 (induced by source paper "fouriernet compact mask representation for instance segmentation using differentiable shape decoders"):
  with members:
    - "embedmask embedding coupling for one stage instance segmentation"
    - "dense reppoints representing visual objects with dense point sets"
    - "polarmask single shot instance segmentation with polar representation"
    - "polytransform deep polygon transformer for instance segmentation"
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "mask scoring r cnn"
    - "hybrid task cascade for instance segmentation"
    - "fcos fully convolutional one stage object detection"

Connected through hyperedge HE-2 (induced by source paper "object contextual representations for semantic segmentation"):
  with members:
    - "learning compositional neural information fusion for human parsing"
    - "acfnet attentional class feature network for semantic segmentation"
    - "dual graph convolutional network for semantic segmentation"
    - "expectation maximization attention networks for semantic segmentation"
    - "global aggregation then local distribution in fully convolutional networks"
    - "gated scnn gated shape cnns for semantic segmentation"
    - "decoders matter for semantic segmentation data dependent decoding enables flexible feature aggregation"
    - "high resolution representations for labeling pixels and regions"

Connected through hyperedge HE-3 (induced by source paper "polytransform deep polygon transformer for instance segmentation"):
  with members:
    - "darnet deep active ray network for building segmentation"
    - "panoptic segmentation"
    - "video instance segmentation"
    - "devil is in the edges learning semantic boundaries from noisy annotations"
    - "adaptis adaptive instance selection network"
    - "mask scoring r cnn"
    - "ssap single shot instance segmentation with affinity pyramid"
    - "hybrid task cascade for instance segmentation"

Connected through hyperedge HE-4 (induced by source paper "acfnet attentional class feature network for semantic segmentation"):
  with members:
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "ocnet object context network for scene parsing"
    - "learning a discriminative feature network for semantic segmentation"
    - "ccnet criss cross attention for semantic segmentation"
    - "autofocus layer for semantic segmentation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "dual attention network for scene segmentation"
    - "context encoding for semantic segmentation"

Connected through hyperedge HE-5 (induced by source paper "centermask real time anchor free instance segmentation"):
  with members:
    - "retinamask learning to predict masks improves state of the art single shot detection for free"
    - "centernet keypoint triplets for object detection"
    - "an energy and gpu computation efficient backbone network for real time object detection"
    - "an empirical study of spatial attention mechanisms in deep networks"
    - "mask scoring r cnn"
    - "high resolution representations for labeling pixels and regions"
    - "tensormask a foundation for dense object segmentation"
    - "fcos fully convolutional one stage object detection"

Connected through hyperedge HE-6 (induced by source paper "the best of both modes separately leveraging rgb and depth for unseen object instance segmentation"):
  with members:
    - "deepim deep iterative matching for 6d pose estimation"
    - "easylabel a semi automatic pixel wise object annotation tool for creating robotic rgb d datasets"
    - "towards segmenting everything that moves"
    - "large scale interactive object segmentation with human annotators"
    - "instance segmentation by jointly optimizing spatial embeddings and clustering bandwidth"
    - "6 dof graspnet variational grasp generation for object manipulation"
    - "group normalization"
    - "deep object pose estimation for semantic robotic grasping of household objects"

Connected through hyperedge HE-7 (induced by source paper "oriented boxes for accurate instance segmentation"):
  with members:
    - "retinamask learning to predict masks improves state of the art single shot detection for free"
    - "mask scoring r cnn"
    - "acquire augment segment enjoy weakly supervised instance segmentation of supermarket products"
    - "arbitrary oriented scene text detection via rotation proposals"
    - "mvtec d2s densely segmented supermarket dataset"
    - "automatic ship detection in remote sensing images from google earth of complex scenes based on multiscale rotation dense feature pyramid networks"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"
    - "path aggregation network for instance 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)