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: "scikit image image processing in python"
  Abstract: scikit-image is an image processing library that implements algorithms and utilities for use in research, education and industry applications. It is released under the liberal Modified BSD open source license, provides a well-documented API in the Python programming language, and is developed by an active, international team of collaborators. In this paper we highlight the advantages of open source to achieve the goals of the scikit-image library, and we showcase several real-world image processing applications that use scikit-image. More information can be found on the project homepage, http://scikit-image.org.

Connected through hyperedge HE-1 (induced by source paper "surveiledge real time video query based on collaborative cloud edge deep learning"):
  with members:
    - "blazeit optimizing declarative aggregation and limit queries for neural network based video analytics"
    - "yolov3 an incremental improvement"
    - "blazeit fast exploratory video queries using neural networks"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "noscope optimizing neural network queries over video at scale"
    - "mask r cnn"
    - "tensorflow a system for large scale machine learning"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-2 (induced by source paper "superpixel segmentation via convolutional neural networks with regularized information maximization"):
  with members:
    - "superpixel sampling networks"
    - "deep image prior"
    - "instance normalization the missing ingredient for fast stylization"
    - "unsupervised monocular depth estimation with left right consistency"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "detection of pitt hopkins syndrome based on morphological facial features"):
  with members:
    - "deepgestalt identifying rare genetic syndromes using deep learning"
    - "facenet a unified embedding for face recognition and clustering"

Connected through hyperedge HE-4 (induced by source paper "improving electron micrograph signal to noise with an atrous convolutional encoder decoder"):
  with members:
    - "regularization for deep learning a taxonomy"
    - "low dose ct image denoising using a generative adversarial network with wasserstein distance and perceptual loss"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "superhuman accuracy on the snemi3d connectomics challenge"
    - "batch renormalization towards reducing minibatch dependence in batch normalized models"
    - "beating the world s best at super smash bros with deep reinforcement learning"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"

Connected through hyperedge HE-5 (induced by source paper "ls net fast single shot line segment detector"):
  with members:
    - "group normalization"
    - "wing loss for robust facial landmark localisation with convolutional neural networks"
    - "focal loss for dense object detection"
    - "richer convolutional features for edge detection"
    - "ssd single shot multibox detector"
    - "understanding data augmentation for classification when to warp"
    - "r fcn object detection via region based fully convolutional networks"
    - "tensorflow a system for large scale machine learning"

Connected through hyperedge HE-6 (induced by source paper "tomogan low dose synchrotron x ray tomography with generative adversarial networks"):
  with members:
    - "a deep journey into super resolution a survey"
    - "denoising imaging polarimetry by adapted bm3d method"
    - "dlhub model and data serving for science"
    - "low dose ct image denoising using a generative adversarial network with wasserstein distance and perceptual loss"
    - "3 d convolutional encoder decoder network for low dose ct via transfer learning from a 2 d trained network"
    - "framing u net via deep convolutional framelets application to sparse view ct"
    - "deep image prior"
    - "deep convolutional neural network for inverse problems in imaging"

Connected through hyperedge HE-7 (induced by source paper "segmentation of roots in soil with u net"):
  with members:
    - "squeeze excite guided few shot segmentation of volumetric images"
    - "flow based self supervised pixel embedding for image segmentation"
    - "on compressing u net using knowledge distillation"
    - "understanding the disharmony between dropout and batch normalization by variance shift"
    - "ternausnet u net with vgg11 encoder pre trained on imagenet for image segmentation"
    - "fully convolutional multi scale residual densenets for cardiac segmentation and automated cardiac diagnosis using ensemble of classifiers"
    - "a systematic study of the class imbalance problem in convolutional neural networks"
    - "cnn based segmentation of medical imaging data"

Connected through hyperedge HE-8 (induced by source paper "colornet investigating the importance of color spaces for image classification"):
  with members:
    - "densely connected convolutional networks"
    - "wide residual networks"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "fast r cnn"
    - "training very deep networks"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "going deeper with convolutions"

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.MS(Mathematical Software)