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 "end to end learning via a convolutional neural network for cancer cell line classification"):
  Abstract: Computer Vision for automated analysis of cells and tissues usually include extracting features from images before analyzing such features via various Machine Learning and Machine Vision algorithms. We developed a Convolutional Neural Network model that classifies MDA-MB-468 and MCF7 breast cancer cells via brightfield microscopy images without the need of any prior feature extraction. Our 6-layer Convolutional Neural Network is directly trained, validated and tested on 1,241 images of MDA-MB-468 and MCF7 breast cancer cell line in an end-to-end fashion, allowing a system to distinguish between different cancer cell types. The model takes in as input imaged breast cancer cell line and then outputs the cell line type (MDA-MB-468 or MCF7) as predicted probabilities between the two classes. Our model scored a 99% Accuracy.

Includes members:
  Member "suggestive annotation a deep active learning framework for biomedical image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "reinforced active learning for image segmentation")
  Member "you only look once unified real time object detection":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "object detection in optical remote sensing images a survey and a new benchmark")
  Member "faster r cnn towards real time object detection with region proposal networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "relatext exploiting visual relationships for arbitrary shaped scene text detection with graph convolutional networks")
  Member "adam a method for stochastic optimization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "semantic image segmentation with deep convolutional nets and fully connected crfs":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "attention guided chained context aggregation for semantic segmentation")
  Member "network in network":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a survey on deep geometry learning from a representation perspective")

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)