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: "an ensemble based system for automatic screening of diabetic retinopathy"
  Abstract: In this paper, an ensemble-based method for the screening of diabetic retinopathy (DR) is proposed. This approach is based on features extracted from the output of several retinal image processing algorithms, such as image-level (quality assessment, pre-screening, AM/FM), lesion-specific (microaneurysms, exudates) and anatomical (macula, optic disk) components. The actual decision about the presence of the disease is then made by an ensemble of machine learning classifiers. We have tested our approach on the publicly available Messidor database, where 90% sensitivity, 91% specificity and 90% accuracy and 0.989 AUC are achieved in a disease/no-disease setting. These results are highly competitive in this field and suggest that retinal image processing is a valid approach for automatic DR screening.

Connected through hyperedge HE-1 (induced by source paper "ophthalmic diagnosis using deep learning with fundus images a critical review"):
  with members:
    - "pixelbnn augmenting the pixelcnn with batch normalization and the presentation of a fast architecture for retinal vessel segmentation"
    - "embedded deep learning in ophthalmology making ophthalmic imaging smarter"
    - "classification of findings with localized lesions in fundoscopic images using a regionally guided cnn"
    - "deep supervision with additional labels for retinal vessel segmentation task"
    - "deep visual domain adaptation a survey"
    - "predicting optical coherence tomography derived diabetic macular edema grades from fundus photographs using deep learning"
    - "deep learning based retinal oct segmentation"
    - "disc aware ensemble network for glaucoma screening from fundus image"

Connected through hyperedge HE-2 (induced by source paper "a simple and efficient architecture for trainable activation functions"):
  with members:
    - "adaptive blending units trainable activation functions for deep neural networks"
    - "self normalizing neural networks"
    - "neural network with unbounded activation functions is universal approximator"
    - "searching for activation functions"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "a single hidden layer feedforward network with only one neuron in the hidden layer can approximate any univariate function"
    - "parametric exponential linear unit for deep convolutional neural networks"
    - "the power of depth for feedforward neural networks"

Connected through hyperedge HE-3 (induced by source paper "vibnn hardware acceleration of bayesian neural networks"):
  with members:
    - "in datacenter performance analysis of a tensor processing unit"
    - "self normalizing neural networks"
    - "bayesian recurrent neural networks"
    - "hardware oriented approximation of convolutional neural networks"
    - "accelerating deep convolutional networks using low precision and sparsity"
    - "eie efficient inference engine on compressed deep neural network"
    - "a new learning method for inference accuracy core occupation and performance co optimization on truenorth chip"
    - "vime variational information maximizing exploration"

Connected through hyperedge HE-4 (induced by source paper "classifier ensemble creation via false labelling"):
  with members:
    - "estimating continuous distributions in bayesian classifiers"
    - "an ensemble based system for microaneurysm detection and diabetic retinopathy grading"

Connected through hyperedge HE-5 (induced by source paper "improved microaneurysm detection using deep neural networks"):
  with members:
    - "a gaussian scale space approach for exudates detection classification and severity prediction"

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)