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: "n 4 fields neural network nearest neighbor fields for image transforms"
  Abstract: We propose a new architecture for difficult image processing operations, such as natural edge detection or thin object segmentation. The architecture is based on a simple combination of convolutional neural networks with the nearest neighbor search. #R##N#We focus our attention on the situations when the desired image transformation is too hard for a neural network to learn explicitly. We show that in such situations, the use of the nearest neighbor search on top of the network output allows to improve the results considerably and to account for the underfitting effect during the neural network training. The approach is validated on three challenging benchmarks, where the performance of the proposed architecture matches or exceeds the state-of-the-art.

Connected through hyperedge HE-1 (induced by source paper "a survey on deep learning based architectures for semantic segmentation on 2d images"):
  with members:
    - "panoptic segmentation"
    - "expectation maximization attention networks for semantic segmentation"
    - "semantic segmentation of video sequences with convolutional lstms"
    - "yolact real time instance segmentation"
    - "convolutional crfs for semantic segmentation"
    - "learning a discriminative feature network for semantic segmentation"
    - "semantic video segmentation a review on recent approaches"
    - "attention u net learning where to look for the pancreas"

Connected through hyperedge HE-2 (induced by source paper "red net a recursive encoder decoder network for edge detection"):
  with members:
    - "casenet deep category aware semantic edge detection"
    - "richer convolutional features for edge detection"
    - "convolutional oriented boundaries from image segmentation to high level tasks"
    - "a recurrent encoder decoder network for sequential face alignment"
    - "learning non lambertian object intrinsics across shapenet categories"
    - "densely connected convolutional networks"
    - "object contour detection with a fully convolutional encoder decoder network"
    - "deep hierarchical parsing for semantic segmentation"

Connected through hyperedge HE-3 (induced by source paper "visualized insights into the optimization landscape of fully convolutional networks"):
  with members:
    - "understanding the loss surface of neural networks for binary classification"
    - "optimization landscape and expressivity of deep cnns"
    - "attentive generative adversarial network for raindrop removal from a single image"
    - "richer convolutional features for edge detection"
    - "visualizing the loss landscape of neural nets"
    - "sharp minima can generalize for deep nets"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "depth creates no bad local minima"

Connected through hyperedge HE-4 (induced by source paper "object contour and edge detection with refinecontournet"):
  with members:
    - "deep crisp boundaries from boundaries to higher level tasks"
    - "edgeconnect generative image inpainting with adversarial edge learning"
    - "learning to predict crisp boundaries"
    - "boundary flow a siamese network that predicts boundary motion without training on motion"
    - "richer convolutional features for edge detection"
    - "multiscale combinatorial grouping for image segmentation and object proposal generation"
    - "convolutional oriented boundaries from image segmentation to high level tasks"
    - "refinenet multi path refinement networks for high resolution semantic segmentation"

Connected through hyperedge HE-5 (induced by source paper "topology maintained structure encoding"):
  with members:
    - "gan dissection visualizing and understanding generative adversarial networks"
    - "universal sentence encoder"
    - "progressive growing of gans for improved quality stability and variation"
    - "richer convolutional features for edge detection"
    - "improved training of wasserstein gans"
    - "high resolution image synthesis and semantic manipulation with conditional gans"
    - "deep ten texture encoding network"
    - "vote3deep fast object detection in 3d point clouds using efficient convolutional neural networks"

Connected through hyperedge HE-6 (induced by source paper "connection sensitive attention u net for accurate retinal vessel segmentation"):
  with members:
    - "attention u net learning where to look for the pancreas"
    - "retinal vessel segmentation in fundoscopic images with generative adversarial networks"
    - "decoupled weight decay regularization"
    - "deepunet a deep fully convolutional network for pixel level sea land segmentation"
    - "iterative deep learning for network topology extraction"
    - "beyond the pixel wise loss for topology aware delineation"
    - "deep retinal image understanding"
    - "holistically nested edge detection"

Connected through hyperedge HE-7 (induced by source paper "joint segmentation and path classification of curvilinear structures"):
  with members:
    - "el gan embedding loss driven generative adversarial networks for lane detection"
    - "roadtracer automatic extraction of road networks from aerial images"
    - "active learning and proofreading for delineation of curvilinear structures"
    - "iterative deep learning for network topology extraction"
    - "beyond the pixel wise loss for topology aware delineation"
    - "deep retinal image understanding"
    - "3d u net learning dense volumetric segmentation from sparse annotation"
    - "holistically nested edge detection"

Connected through hyperedge HE-8 (induced by source paper "dense extreme inception network towards a robust cnn model for edge detection"):
  with members:
    - "bi directional cascade network for perceptual edge detection"
    - "residual dense network for image super resolution"
    - "richer convolutional features for edge detection"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "xception deep learning with depthwise separable convolutions"
    - "tensorflow a system for large scale machine learning"
    - "holistically nested edge detection"
    - "very deep convolutional networks for large scale image recognition"

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)