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 "camera model identification using convolutional neural networks"):
  Abstract: Source camera identification is the process of determining which camera or model has been used to capture an image. In the recent years, there has been a rapid growth of research interest in the domain of forensics. In the current work, we describe our Deep Learning approach to the camera detection task of 10 cameras as a part of the Camera Model Identification Challenge hosted by this http URL where our team finished 2nd out of 582 teams with the accuracy on the unseen data of 98%. We used aggressive data augmentations that allowed a model to stay robust against transformations. A number of experiments are carried out on datasets collected by organizers and scraped from the web.

Includes members:
  Member "albumentations fast and flexible image augmentations":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "neural mesh refiner for 6 dof pose estimation")
  Member "deep convolutional neural networks for breast cancer histology image analysis":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multi scale domain adversarial multiple instance cnn for cancer subtype classification with non annotated histopathological images")
  Member "paediatric bone age assessment using deep convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "albumentations fast and flexible image augmentations")
  Member "angiodysplasia detection and localization using deep convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "albumentations fast and flexible image augmentations")
  Member "fully convolutional network for automatic road extraction from satellite imagery":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "albumentations fast and flexible image augmentations")
  Member "automatic instrument segmentation in robot assisted surgery using deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "ap mtl attention pruned multi task learning model for real time instrument detection and segmentation in robot assisted surgery")
  Member "ternausnetv2 fully convolutional network for instance segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "abdominal multi organ segmentation with cascaded convolutional and adversarial deep networks")
  Member "feature pyramid network for multi class land segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "dense dilated convolutions merging network for land cover classification")

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)