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: "lvis a dataset for large vocabulary instance segmentation"
  Abstract: Progress on object detection is enabled by datasets that focus the research community's attention on open challenges. This process led us from simple images to complex scenes and from bounding boxes to segmentation masks. In this work, we introduce LVIS (pronounced `el-vis'): a new dataset for Large Vocabulary Instance Segmentation. We plan to collect ~2 million high-quality instance segmentation masks for over 1000 entry-level object categories in 164k images. Due to the Zipfian distribution of categories in natural images, LVIS naturally has a long tail of categories with few training samples. Given that state-of-the-art deep learning methods for object detection perform poorly in the low-sample regime, we believe that our dataset poses an important and exciting new scientific challenge. LVIS is available at this http URL.

Connected through hyperedge HE-1 (induced by source paper "equalization loss for long tailed object recognition"):
  with members:
    - "decoupling representation and classifier for long tailed recognition"
    - "large scale long tailed recognition in an open world"
    - "class balanced loss based on effective number of samples"
    - "mask scoring r cnn"
    - "meta weight net learning an explicit mapping for sample weighting"
    - "grid r cnn"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"
    - "exploring the limits of weakly supervised pretraining"

Connected through hyperedge HE-2 (induced by source paper "blendmask top down meets bottom up for instance segmentation"):
  with members:
    - "pointrend image segmentation as rendering"
    - "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"
    - "solo segmenting objects by locations"

Connected through hyperedge HE-3 (induced by source paper "frustratingly simple few shot object detection"):
  with members:
    - "a baseline for few shot image classification"
    - "tafe net task aware feature embeddings for low shot learning"
    - "a closer look at few shot classification"
    - "on first order meta learning algorithms"
    - "meta learning with latent embedding optimization"
    - "few shot object detection via feature reweighting"
    - "dynamic few shot visual learning without forgetting"
    - "low shot learning with imprinted weights"

Connected through hyperedge HE-4 (induced by source paper "f brs rethinking backpropagating refinement for interactive segmentation"):
  with members:
    - "large scale interactive object segmentation with human annotators"
    - "interactive full image segmentation by considering all regions jointly"
    - "bag of tricks for image classification with convolutional neural networks"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "dynamic zoom in network for fast object detection in large images"
    - "deep interactive object selection"
    - "texture synthesis using convolutional neural networks"
    - "adaptive object detection using adjacency and zoom prediction"

Connected through hyperedge HE-5 (induced by source paper "momentum contrast for unsupervised visual representation learning"):
  with members:
    - "scaling and benchmarking self supervised visual representation learning"
    - "contrastive multiview coding"
    - "large scale adversarial representation learning"
    - "unsupervised pre training of image features on non curated data"
    - "data efficient image recognition with contrastive predictive coding"
    - "fast autoaugment"
    - "local aggregation for unsupervised learning of visual embeddings"
    - "learning representations by maximizing mutual information across views"

Connected through hyperedge HE-6 (induced by source paper "controlling style and semantics in weakly supervised image generation"):
  with members:
    - "generating multiple objects at spatially distinct locations"
    - "semantic image synthesis with spatially adaptive normalization"
    - "cgans with projection discriminator"
    - "chatpainter improving text to image generation using dialogue"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "tell draw and repeat generating and modifying images based on continual linguistic instruction"
    - "finegan unsupervised hierarchical disentanglement for fine grained object generation and discovery"
    - "spectral normalization for generative adversarial networks"

Connected through hyperedge HE-7 (induced by source paper "mixture dense regression for object detection and human pose estimation"):
  with members:
    - "rethinking on multi stage networks for human pose estimation"
    - "overcoming limitations of mixture density networks a sampling and fitting framework for multimodal future prediction"
    - "gaussian yolov3 an accurate and fast object detector using localization uncertainty for autonomous driving"
    - "feature selective anchor free module for single shot object detection"
    - "unsupervised discovery of parts structure and dynamics"
    - "pifpaf composite fields for human pose estimation"
    - "scale aware trident networks for object detection"
    - "objects as points"

Connected through hyperedge HE-8 (induced by source paper "pointrend image segmentation as rendering"):
  with members:
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "high resolution representations for labeling pixels and regions"
    - "tensormask a foundation for dense object segmentation"
    - "panoptic feature pyramid networks"
    - "efficient segmentation learning downsampling near semantic boundaries"
    - "rethinking imagenet pre training"
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "encoder decoder with atrous separable convolution for semantic 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)