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: "fastmask segment multi scale object candidates in one shot"
  Abstract: Objects appear to scale differently in natural images. This fact requires methods dealing with object-centric tasks (e.g. object proposal) to have robust performance over variances in object scales. In the paper, we present a novel segment proposal framework, namely FastMask, which takes advantage of hierarchical features in deep convolutional neural networks to segment multi-scale objects in one shot. Innovatively, we adapt segment proposal network into three different functional components (body, neck and head). We further propose a weight-shared residual neck module as well as a scale-tolerant attentional head module for efficient one-shot inference. On MS COCO benchmark, the proposed FastMask outperforms all state-of-the-art segment proposal methods in average recall being 2~5 times faster. Moreover, with a slight trade-off in accuracy, FastMask can segment objects in near real time (~13 fps) with 800*600 resolution images, demonstrating its potential in practical applications. Our implementation is available on this https URL.

Connected through hyperedge HE-1 (induced by source paper "semi convolutional operators for instance segmentation"):
  with members:
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "segmentation aware convolutional networks using local attention masks"
    - "recurrent pixel embedding for instance grouping"
    - "pixelwise instance segmentation with a dynamically instantiated network"
    - "semantic instance segmentation via deep metric learning"
    - "mask r cnn"
    - "instance sensitive fully convolutional networks"
    - "pixel level encoding and depth layering for instance level semantic labeling"

Connected through hyperedge HE-2 (induced by source paper "attentionmask attentive efficient object proposal generation focusing on small objects"):
  with members:
    - "deeply supervised salient object detection with short connections"
    - "zoom out and in network with recursive training for object proposal"
    - "masklab instance segmentation by refining object detection with semantic and direction features"
    - "ron reverse connection with objectness prior networks for object detection"
    - "multiscale combinatorial grouping for image segmentation and object proposal generation"
    - "mask r cnn"
    - "scalenet guiding object proposal generation in supermarkets and beyond"
    - "instance sensitive fully convolutional networks"

Connected through hyperedge HE-3 (induced by source paper "acquisition of localization confidence for accurate object detection"):
  with members:
    - "soft nms improving object detection with one line of code"
    - "relation networks for object detection"
    - "cascade r cnn delving into high quality object detection"
    - "learning non maximum suppression"
    - "repulsion loss detecting pedestrians in a crowd"
    - "mask r cnn"
    - "learning to refine object segments"
    - "feature pyramid networks for object detection"

Connected through hyperedge HE-4 (induced by source paper "context proposals for saliency detection"):
  with members:
    - "deeply supervised salient object detection with short connections"
    - "deep contrast learning for salient object detection"
    - "a review of co saliency detection technique fundamentals applications and challenges"
    - "learning to refine object segments"
    - "what makes for effective detection proposals"
    - "shallow and deep convolutional networks for saliency prediction"
    - "deepsaliency multi task deep neural network model for salient object detection"
    - "visual saliency based on multiscale deep features"

Connected through hyperedge HE-5 (induced by source paper "examining deep learning architectures for crime classification and prediction"):
  with members:
    - "deep spatio temporal residual networks for citywide crowd flows prediction"
    - "deep residual learning for image recognition"
    - "modeling spatial temporal clues in a hybrid deep learning framework for video classification"
    - "very deep convolutional networks for large scale image recognition"
    - "imagenet large scale visual recognition challenge"
    - "scikit learn machine learning in python"

Connected through hyperedge HE-6 (induced by source paper "recurrent pixel embedding for instance grouping"):
  with members:
    - "segmentation aware convolutional networks using local attention masks"
    - "sampling matters in deep embedding learning"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "focal loss for dense object detection"
    - "pixelwise instance segmentation with a dynamically instantiated network"
    - "recurrent scene parsing with perspective understanding in the loop"
    - "semantic instance segmentation via deep metric learning"
    - "semantic instance segmentation with a discriminative loss function"

Connected through hyperedge HE-7 (induced by source paper "semantic instance segmentation via deep metric learning"):
  with members:
    - "instance sensitive fully convolutional networks"
    - "pixel level encoding and depth layering for instance level semantic labeling"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "deep watershed transform for instance segmentation"
    - "instancecut from edges to instances with multicut"
    - "what makes for effective detection proposals"
    - "feature pyramid networks for object detection"
    - "end to end instance segmentation with recurrent attention"

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)