human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "amd severity prediction and explainability using image registration and deep embedded clustering"):
  Abstract: We propose a method to predict severity of age related macular degeneration (AMD) from input optical coherence tomography (OCT) images. Although there is no standard clinical severity scale for AMD, we leverage deep learning (DL) based image registration and clustering methods to identify diseased cases and predict their severity. Experiments demonstrate our approach's disease classification performance matches state of the art methods. The predicted disease severity performs well on previously unseen data. Registration output provides better explainability than class activation maps regarding label and severity decisions

Includes members:
  Member "regression concept vectors for bidirectional explanations in histopathology":
    also connected through other hyperedges:
      (none)
  Member "informative sample generation using class aware generative adversarial networks for classification of chest xrays":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "efficient active learning for image classification and segmentation using a sample selection and conditional generative adversarial network")
  Member "training data independent image registration with gans using transfer learning and segmentation information":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "informative sample generation using class aware generative adversarial networks for classification of chest xrays")
  Member "automatic brain tumor grading from mri data using convolutional neural networks and quality assessment":
    also connected through other hyperedges:
      (none)
  Member "efficient active learning for image classification and segmentation using a sample selection and conditional generative adversarial network":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "medgan medical image translation using gans")
  Member "an unsupervised learning model for deformable medical image registration":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "when deep learning meets data alignment a review on deep registration networks drns")
  Member "end to end unsupervised deformable image registration with a convolutional neural network":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "unsupervised multi modal image registration via geometry preserving image to image translation")
  Member "densely connected convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "lossless compression of deep neural networks")

Question: Please tell me which class the source paper 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)