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: "hyperparameters optimization in deep convolutional neural network bayesian approach with gaussian process prior"
  Abstract: Convolutional Neural Network is known as ConvNet have been extensively used in many complex machine learning tasks. However, hyperparameters optimization is one of a crucial step in developing ConvNet architectures, since the accuracy and performance are reliant on the hyperparameters. This multilayered architecture parameterized by a set of hyperparameters such as the number of convolutional layers, number of fully connected dense layers & neurons, the probability of dropout implementation, learning rate. Hence the searching the hyperparameter over the hyperparameter space are highly difficult to build such complex hierarchical architecture. Many methods have been proposed over the decade to explore the hyperparameter space and find the optimum set of hyperparameter values. Reportedly, Gird search and Random search are said to be inefficient and extremely expensive, due to a large number of hyperparameters of the architecture. Hence, Sequential model-based Bayesian Optimization is a promising alternative technique to address the extreme of the unknown cost function. The recent study on Bayesian Optimization by Snoek in nine convolutional network parameters is achieved the lowerest error report in the CIFAR-10 benchmark. This article is intended to provide the overview of the mathematical concept behind the Bayesian Optimization over a Gaussian prior.

Connected through hyperedge HE-1 (induced by source paper "interactive subspace exploration on generative image modelling"):
  with members:
    - "faceshop deep sketch based face image editing"
    - "multimodal unsupervised image to image translation"
    - "gan dissection visualizing and understanding generative adversarial networks"
    - "progressive growing of gans for improved quality stability and variation"
    - "attngan fine grained text to image generation with attentional generative adversarial networks"
    - "real time user guided image colorization with learned deep priors"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "image to image translation with conditional adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "image synthesis in multi contrast mri with conditional generative adversarial networks"):
  with members:
    - "an introduction to image synthesis with generative adversarial nets"
    - "gan based synthetic medical image augmentation for increased cnn performance in liver lesion classification"
    - "unsupervised reverse domain adaptation for synthetic medical images via adversarial training"
    - "speeding up the hyperparameter optimization of deep convolutional neural networks"
    - "synthesis of positron emission tomography pet images via multi channel generative adversarial networks gans"
    - "freehand ultrasound image simulation with spatially conditioned generative adversarial networks"
    - "denoising of 3d magnetic resonance images with multi channel residual learning of convolutional neural network"
    - "virtual pet images from ct data using deep convolutional networks initial results"

Connected through hyperedge HE-3 (induced by source paper "combo loss handling input and output imbalance in multi organ segmentation"):
  with members:
    - "deepigeos a deep interactive geodesic framework for medical image segmentation"
    - "revisiting small batch training for deep neural networks"
    - "focal loss for dense object detection"
    - "an exploration of 2d and 3d deep learning techniques for cardiac mr image segmentation"
    - "generalised dice overlap as a deep learning loss function for highly unbalanced segmentations"
    - "a survey on deep learning in medical image analysis"
    - "3d u net learning dense volumetric segmentation from sparse annotation"
    - "hough cnn deep learning for segmentation of deep brain regions in mri and ultrasound"

Connected through hyperedge HE-4 (induced by source paper "facial information recovery from heavily damaged images using generative adversarial network part 1"):
  with members:
    - "implementation of deep convolutional neural network in multi class categorical image classification"
    - "feed forward and backward run in deep convolution neural network"
    - "deep generative filter for motion deblurring"
    - "regularization and optimization strategies in deep convolutional neural network"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "improved techniques for training gans"
    - "semantic image inpainting with deep generative models"
    - "semantic image inpainting with perceptual and contextual losses"

Connected through hyperedge HE-5 (induced by source paper "gradient target propagation"):
  with members:
    - "biologically motivated algorithms for propagating local target representations"
    - "speeding up the hyperparameter optimization of deep convolutional neural networks"
    - "on weight initialization in deep neural networks"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "a theory of local learning the learning channel and the optimality of backpropagation"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "how auto encoders could provide credit assignment in deep networks via target propagation"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "implementation of deep convolutional neural network in multi class categorical image classification"):
  with members:
    - "feed forward and backward run in deep convolution neural network"
    - "early stopping without a validation set"
    - "regularization and optimization strategies in deep convolutional neural network"
    - "deep networks with stochastic depth"
    - "deep residual learning for image recognition"
    - "auto weka combined selection and hyperparameter optimization of classification algorithms"
    - "improving neural networks by preventing co adaptation of feature detectors"
    - "a tutorial on bayesian optimization of expensive cost functions with application to active user modeling and hierarchical reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "learning the sequential temporal information with recurrent neural networks"):
  with members:
    - "implementation of deep convolutional neural network in multi class categorical image classification"
    - "feed forward and backward run in deep convolution neural network"
    - "regularization and optimization strategies in deep convolutional neural network"

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)