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: "reversible recursive instance level object segmentation"
  Abstract: In this work, we propose a novel Reversible Recursive Instance-level Object Segmentation (R2-IOS) framework to address the challenging instance-level object segmentation task. R2-IOS consists of a reversible proposal refinement sub-network that predicts bounding box offsets for refining the object proposal locations, and an instance-level segmentation sub-network that generates the foreground mask of the dominant object instance in each proposal. By being recursive, R2-IOS iteratively optimizes the two sub-networks during joint training, in which the refined object proposals and improved segmentation predictions are alternately fed into each other to progressively increase the network capabilities. By being reversible, the proposal refinement sub-network adaptively determines an optimal number of refinement iterations required for each proposal during both training and testing. Furthermore, to handle multiple overlapped instances within a proposal, an instance-aware denoising autoencoder is introduced into the segmentation sub-network to distinguish the dominant object from other distracting instances. Extensive experiments on the challenging PASCAL VOC 2012 benchmark well demonstrate the superiority of R2-IOS over other state-of-the-art methods. In particular, the $\text{AP}^r$ over $20$ classes at $0.5$ IoU achieves $66.7\%$, which significantly outperforms the results of $58.7\%$ by PFN~\cite{PFN} and $46.3\%$ by~\cite{liu2015multi}.

Connected through hyperedge HE-1 (induced by source paper "instance segmentation with point supervision"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "retinamask learning to predict masks improves state of the art single shot detection for free"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "a generalized framework for agglomerative clustering of signed graphs applied to instance segmentation"):
  with members:
    - "the mutex watershed and its objective efficient parameter free image partitioning"
    - "instance segmentation by deep coloring"
    - "affinity derivation and graph merge for instance segmentation"
    - "path aggregation network for instance segmentation"
    - "cell detection with star convex polygons"
    - "superhuman accuracy on the snemi3d connectomics challenge"
    - "anisotropic em segmentation by 3d affinity learning and agglomeration"
    - "recurrent pixel embedding for instance grouping"

Connected through hyperedge HE-3 (induced by source paper "connnet a long range relation aware pixel connectivity network for salient segmentation"):
  with members:
    - "deeply supervised salient object detection with short connections"
    - "non local neural networks"
    - "blitznet a real time deep network for scene understanding"
    - "supervised adversarial networks for image saliency detection"
    - "picanet learning pixel wise contextual attention for saliency detection"
    - "instance level salient object segmentation"
    - "salgan visual saliency prediction with generative adversarial networks"
    - "mask r cnn"

Connected through hyperedge HE-4 (induced by source paper "vm net mesh modeling to assist segmentation in volumetric data"):
  with members:
    - "neuralsampler euclidean point cloud auto encoder and sampler"
    - "data augmentation using learned transformations for one shot medical image segmentation"
    - "a large annotated medical image dataset for the development and evaluation of segmentation algorithms"
    - "pixel2mesh multi view 3d mesh generation via deformation"
    - "deep mesh reconstruction from single rgb images via topology modification networks"
    - "deep sequential segmentation of organs in volumetric medical scans"
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "recurrent saliency transformation network incorporating multi stage visual cues for small organ segmentation"

Connected through hyperedge HE-5 (induced by source paper "a method for arbitrary instance style transfer"):
  with members:
    - "class based styling real time localized style transfer with semantic segmentation"
    - "path aggregation network for instance segmentation"
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "son of zorn s lemma targeted style transfer using instance aware semantic segmentation"
    - "universal style transfer via feature transforms"
    - "stylebank an explicit representation for neural image style transfer"
    - "mask r cnn"
    - "perceptual losses for real time style transfer and super resolution"

Connected through hyperedge HE-6 (induced by source paper "learning deep representations for semantic image parsing a comprehensive overview"):
  with members:
    - "large kernel matters improve semantic segmentation by global convolutional network"
    - "deep supervision for pancreatic cyst segmentation in abdominal ct scans"
    - "progressively diffused networks for semantic image segmentation"
    - "not all pixels are equal difficulty aware semantic segmentation via deep layer cascade"
    - "knowledge guided recurrent neural network learning for task oriented action prediction"
    - "interpretable structure evolving lstm"
    - "label refinement network for coarse to fine semantic segmentation"
    - "instance level salient object segmentation"

Connected through hyperedge HE-7 (induced by source paper "path aggregation network for instance segmentation"):
  with members:
    - "focal loss for dense object detection"
    - "large kernel matters improve semantic segmentation by global convolutional network"
    - "pixelwise instance segmentation with a dynamically instantiated network"
    - "dssd deconvolutional single shot detector"
    - "deformable convolutional networks"
    - "squeeze and excitation networks"
    - "dual path networks"
    - "mask r cnn"

Connected through hyperedge HE-8 (induced by source paper "adaptive temporal encoding network for video instance level human parsing"):
  with members:
    - "look into person joint body parsing pose estimation network and a new benchmark"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "visual question reasoning on general dependency tree"
    - "dual motion gan for future flow embedded video prediction"
    - "holistic instance level human parsing"
    - "look into person self supervised structure sensitive learning and a new benchmark for human parsing"
    - "flow guided feature aggregation for video object detection"
    - "deep variation structured reinforcement learning for visual relationship and attribute detection"

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)