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 "unsupervised learning for large scale fiber detection and tracking in microscopic material images"):
  Abstract: Constructing 3D structures from serial section data is a long standing problem in microscopy. The structure of a fiber reinforced composite material can be reconstructed using a tracking-by-detection model. Tracking-by-detection algorithms rely heavily on detection accuracy, especially the recall performance. The state-of-the-art fiber detection algorithms perform well under ideal conditions, but are not accurate where there are local degradations of image quality, due to contaminants on the material surface and/or defocus blur. Convolutional Neural Networks (CNN) could be used for this problem, but would require a large number of manual annotated fibers, which are not available. We propose an unsupervised learning method to accurately detect fibers on the large scale, that is robust against local degradations of image quality. The proposed method does not require manual annotations, but uses fiber shape/size priors and spatio-temporal consistency in tracking to simulate the supervision in the training of the CNN. Experiments show significant improvements over state-of-the-art fiber detection algorithms together with advanced tracking performance.

Includes members:
  Member "unsupervised learning from video to detect foreground objects in single images":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning video object segmentation from unlabeled videos")
  Member "detect to track and track to detect":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "stem seg spatio temporal embeddings for instance segmentation in videos")
  Member "unsupervised representation learning by sorting sequences":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning representations by predicting bags of visual words")
  Member "learning to segment human by watching youtube":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "few example object detection with model communication")
  Member "is faster r cnn doing well for pedestrian detection":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "psc net learning part spatial co occurence for occluded pedestrian detection")
  Member "finding tiny faces":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "kpnet towards minimal face detector")
  Member "unsupervised learning of edges":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "implicit label augmentation on partially annotated clips via temporally adaptive features learning")
  Member "unsupervised learning of visual representations using videos":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "mast a memory augmented self supervised tracker")

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)