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 "sfnet learning object aware semantic correspondence"):
  Abstract: We address the problem of semantic correspondence, that is, establishing a dense flow field between images depicting different instances of the same object or scene category. We propose to use images annotated with binary foreground masks and subjected to synthetic geometric deformations to train a convolutional neural network (CNN) for this task. Using these masks as part of the supervisory signal offers a good compromise between semantic flow methods, where the amount of training data is limited by the cost of manually selecting point correspondences, and semantic alignment ones, where the regression of a single global geometric transformation between images may be sensitive to image-specific details such as background clutter. We propose a new CNN architecture, dubbed SFNet, which implements this idea. It leverages a new and differentiable version of the argmax function for end-to-end training, with a loss that combines mask and flow consistency with smoothness terms. Experimental results demonstrate the effectiveness of our approach, which significantly outperforms the state of the art on standard benchmarks.

Includes members:
  Member "df net unsupervised joint learning of depth and flow using cross task consistency":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "crdoco pixel level domain transfer with cross domain consistency")
  Member "self supervised learning of geometrically stable features through probabilistic introspection":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "robust 6d object pose estimation by learning rgb d features")
  Member "attentive semantic alignment with offset aware correlation kernels":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a two stream symmetric network with bidirectional ensemble for aerial image matching")
  Member "end to end weakly supervised semantic alignment":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "os2d one stage one shot object detection by matching anchor features")
  Member "convolutional neural network architecture for geometric matching":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "os2d one stage one shot object detection by matching anchor features")
  Member "improving landmark localization with semi supervised learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "self supervised single view 3d reconstruction via semantic consistency")
  Member "dctm discrete continuous transformation matching for semantic flow":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a two stream symmetric network with bidirectional ensemble for aerial image matching")
  Member "fcss fully convolutional self similarity for dense semantic correspondence":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a two stream symmetric network with bidirectional ensemble for aerial image matching")

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)