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: "deep learning based computer aided diagnosis systems for diabetic retinopathy a survey"
  Abstract: Diabetic retinopathy (DR) results in vision loss if not treated early. A computer-aided diagnosis (CAD) system based on retinal fundus images is an efficient and effective method for early DR diagnosis and assisting experts. A computer-aided diagnosis (CAD) system involves various stages like detection, segmentation and classification of lesions in fundus images. Many traditional machine-learning (ML) techniques based on hand-engineered features have been introduced. The recent emergence of deep learning (DL) and its decisive victory over traditional ML methods for various applications motivated the researchers to employ it for DR diagnosis, and many deep-learning-based methods have been introduced. In this paper, we review these methods, highlighting their pros and cons. In addition, we point out the challenges to be addressed in designing and learning about efficient, effective and robust deep-learning algorithms for various problems in DR diagnosis and draw attention to directions for future research.

Connected through hyperedge HE-1 (induced by source paper "deep learning approach to diabetic retinopathy detection"):
  with members:
    - "albumentations fast and flexible image augmentations"
    - "on the variance of the adaptive learning rate and beyond"
    - "transfer learning based detection of diabetic retinopathy from small dataset"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "on first order meta learning algorithms"
    - "ternausnet u net with vgg11 encoder pre trained on imagenet for image segmentation"
    - "grader variability and the importance of reference standards for evaluating machine learning models for diabetic retinopathy"
    - "focal loss for dense object detection"

Connected through hyperedge HE-2 (induced by source paper "transfer learning based detection of diabetic retinopathy from small dataset"):
  with members:
    - "deep learning on small datasets without pre training using cosine loss"
    - "deep learning vs human graders for classifying severity levels of diabetic retinopathy in a real world nationwide screening program"
    - "deep image mining for diabetic retinopathy screening"
    - "a survey on deep learning in medical image analysis"
    - "weakly supervised localization of diabetic retinopathy lesions in retinal fundus images"
    - "rethinking the inception architecture for computer vision"

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)