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: "retinamask learning to predict masks improves state of the art single shot detection for free"
  Abstract: Recently two-stage detectors have surged ahead of single-shot detectors in the accuracy-vs-speed trade-off. Nevertheless single-shot detectors are immensely popular in embedded vision applications. This paper brings single-shot detectors up to the same level as current two-stage techniques. We do this by improving training for the state-of-the-art single-shot detector, RetinaNet, in three ways: integrating instance mask prediction for the first time, making the loss function adaptive and more stable, and including additional hard examples in training. We call the resulting augmented network RetinaMask. The detection component of RetinaMask has the same computational cost as the original RetinaNet, but is more accurate. COCO test-dev results are up to 41.4 mAP for RetinaMask-101 vs 39.1mAP for RetinaNet-101, while the runtime is the same during evaluation. Adding Group Normalization increases the performance of RetinaMask-101 to 41.7 mAP. Code is at:this https URL

Connected through hyperedge HE-1 (induced by source paper "multi task collaborative network for joint referring expression comprehension and segmentation"):
  with members:
    - "a real time global inference network for one stage referring expression comprehension"
    - "zero shot grounding of objects from natural language queries"
    - "cross modal self attention network for referring image segmentation"
    - "yolact real time instance segmentation"
    - "dynamic multimodal instance segmentation guided by natural language queries"
    - "neighbourhood watch referring expression comprehension via language guided graph attention networks"
    - "yolov3 an incremental improvement"
    - "mattnet modular attention network for referring expression comprehension"

Connected through hyperedge HE-2 (induced by source paper "on the evaluation and real world usage scenarios of deep vessel segmentation for retinography"):
  with members:
    - "dunet a deformable network for retinal vessel segmentation"
    - "adaptive gradient methods with dynamic bound of learning rate"
    - "data efficient image recognition with contrastive predictive coding"
    - "fcos fully convolutional one stage object detection"
    - "mixmatch a holistic approach to semi supervised learning"
    - "ternausnetv2 fully convolutional network for instance segmentation"
    - "deep retinal image understanding"
    - "holistically nested edge detection"

Connected through hyperedge HE-3 (induced by source paper "instance segmentation with point supervision"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "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"
    - "yolov3 an incremental improvement"

Connected through hyperedge HE-4 (induced by source paper "spatialflow bridging all tasks for panoptic segmentation"):
  with members:
    - "panoptic segmentation"
    - "an end to end network for panoptic segmentation"
    - "region proposal by guided anchoring"
    - "upsnet a unified panoptic segmentation network"
    - "box driven class wise region masking and filling rate guided loss for weakly supervised semantic segmentation"
    - "deeperlab single shot image parser"
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "hybrid task cascade for instance segmentation"

Connected through hyperedge HE-5 (induced by source paper "yolact better real time instance segmentation"):
  with members:
    - "mask scoring r cnn"
    - "yolact real time instance segmentation"
    - "instance segmentation by jointly optimizing spatial embeddings and clustering bandwidth"
    - "yolov3 an incremental improvement"
    - "deformable convnets v2 more deformable better results"
    - "path aggregation network for instance segmentation"
    - "segmentation aware convolutional networks using local attention masks"
    - "focal loss for dense object detection"

Connected through hyperedge HE-6 (induced by source paper "yolact real time instance segmentation"):
  with members:
    - "mask scoring r cnn"
    - "yolov3 an incremental improvement"
    - "path aggregation network for instance segmentation"
    - "segmentation aware convolutional networks using local attention masks"
    - "focal loss for dense object detection"
    - "blitznet a real time deep network for scene understanding"
    - "icnet for real time semantic segmentation on high resolution images"
    - "enet a deep neural network architecture for real time semantic segmentation"

Connected through hyperedge HE-7 (induced by source paper "centermask real time anchor free instance segmentation"):
  with members:
    - "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"
    - "yolact real time instance segmentation"

Connected through hyperedge HE-8 (induced by source paper "where are the masks instance segmentation with image level supervision"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "instance segmentation with point supervision"
    - "instance segmentation of fibers from low resolution ct scans via 3d deep embedding learning"
    - "yolact real time instance segmentation"
    - "object counting and instance segmentation with image level supervision"
    - "weakly supervised instance segmentation using class peak response"
    - "where are the blobs counting by localization with point supervision"
    - "learning pixel level semantic affinity with image level supervision for weakly supervised semantic 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)