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: "solo segmenting objects by locations"
  Abstract: We present a new, embarrassingly simple approach to instance segmentation in images. Compared to many other dense prediction tasks, e.g., semantic segmentation, it is the arbitrary number of instances that have made instance segmentation much more challenging. In order to predict a mask for each instance, mainstream approaches either follow the 'detect-thensegment' strategy as used by Mask R-CNN, or predict category masks first then use clustering techniques to group pixels into individual instances. We view the task of instance segmentation from a completely new perspective by introducing the notion of "instance categories", which assigns categories to each pixel within an instance according to the instance's location and size, thus nicely converting instance mask segmentation into a classification-solvable problem. Now instance segmentation is decomposed into two classification tasks. We demonstrate a much simpler and flexible instance segmentation framework with strong performance, achieving on par accuracy with Mask R-CNN and outperforming recent singleshot instance segmenters in accuracy. We hope that this very simple and strong framework can serve as a baseline for many instance-level recognition tasks besides instance segmentation.

Connected through hyperedge HE-1 (induced by source paper "neural mesh refiner for 6 dof pose estimation"):
  with members:
    - "deepim deep iterative matching for 6d pose estimation"
    - "albumentations fast and flexible image augmentations"
    - "how much position information do convolutional neural networks encode"
    - "disentangling monocular 3d object detection"
    - "c3dpo canonical 3d pose networks for non rigid structure from motion"
    - "deep fitting degree scoring network for monocular 3d object detection"
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "6d object pose estimation based on 2d bounding box"

Connected through hyperedge HE-2 (induced by source paper "pointins point based instance segmentation"):
  with members:
    - "deep snake for real time instance segmentation"
    - "embedmask embedding coupling for one stage instance segmentation"
    - "polarmask single shot instance segmentation with polar representation"
    - "mask scoring r cnn"
    - "tensormask a foundation for dense object segmentation"
    - "hybrid task cascade for instance segmentation"
    - "fcos fully convolutional one stage object detection"
    - "yolact real time instance segmentation"

Connected through hyperedge HE-3 (induced by source paper "blendmask top down meets bottom up for instance segmentation"):
  with members:
    - "pointrend image segmentation as rendering"
    - "lvis a dataset for large vocabulary instance segmentation"
    - "polarmask single shot instance segmentation with polar representation"
    - "yolact better real time instance segmentation"
    - "imp instance mask projection for high accuracy semantic segmentation of things"
    - "tensormask a foundation for dense object segmentation"
    - "fcos fully convolutional one stage object detection"
    - "yolact real time instance segmentation"

Connected through hyperedge HE-4 (induced by source paper "sdod real time segmenting and detecting 3d objects by depth"):
  with members:
    - "sdnet semantically guided depth estimation network"
    - "yolact better real time instance segmentation"
    - "mask scoring r cnn"
    - "fcos fully convolutional one stage object detection"
    - "monocular 3d object detection leveraging accurate proposals and shape reconstruction"
    - "efficient interactive annotation of segmentation datasets with polygon rnn"
    - "deep ordinal regression network for monocular depth estimation"
    - "bdd100k a diverse driving video database with scalable annotation tooling"

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)