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 "self supervised unconstrained illumination invariant representation"):
  Abstract: We propose a new and completely data-driven approach for generating an unconstrained illumination invariant representation of images. Our method trains a neural network with a specialized triplet loss designed to emphasize actual scene changes while downplaying changes in illumination. For this purpose we use the BigTime image dataset, which contains static scenes acquired at different times. We analyze the attributes of our representation, and show that it improves patch matching and rigid registration over state-of-the-art illumination invariant representations. We point out that the utility of our method is not restricted to handling illumination invariance, and that it may be applied for generating representations which are invariant to general types of nuisance, undesired, image variants.

Includes members:
  Member "learning intrinsic image decomposition from watching the world":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "implicit generation and generalization in energy based models")
  Member "unsupervised deep single image intrinsic decomposition using illumination varying image sequences":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "shadingnet image intrinsics by fine grained shading decomposition")
  Member "in defense of the triplet loss for person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "stem seg spatio temporal embeddings for instance segmentation in videos")
  Member "u net convolutional networks for biomedical image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "secure and robust machine learning for healthcare a survey")
  Member "learning local image descriptors with deep siamese and triplet convolutional networks by minimising global loss functions":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "when relation networks meet gans relation gans with triplet loss")
  Member "going deeper with convolutions":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "deep metric learning using triplet network":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "opengan open set generative adversarial 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)