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 weakly supervised semantic alignment"):
  Abstract: We tackle the task of semantic alignment where the goal is to compute dense semantic correspondence aligning two images depicting objects of the same category. This is a challenging task due to large intra-class variation, changes in viewpoint and background clutter. We present the following three principal contributions. First, we develop a convolutional neural network architecture for semantic alignment that is trainable in an end-to-end manner from weak image-level supervision in the form of matching image pairs. The outcome is that parameters are learnt from rich appearance variation present in different but semantically related images without the need for tedious manual annotation of correspondences at training time. Second, the main component of this architecture is a differentiable soft inlier scoring module, inspired by the RANSAC inlier scoring procedure, that computes the quality of the alignment based on only geometrically consistent correspondences thereby reducing the effect of background clutter. Third, we demonstrate that the proposed approach achieves state-of-the-art performance on multiple standard benchmarks for semantic alignment.

Includes members:
  Member "convolutional neural network architecture for geometric matching":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "os2d one stage one shot object detection by matching anchor features")
  Member "anchornet a weakly supervised network to learn geometry sensitive features for semantic matching":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning correspondence from the cycle consistency of time")
  Member "fcss fully convolutional self similarity for dense semantic correspondence":
    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 "scnet learning semantic correspondence":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a two stream symmetric network with bidirectional ensemble for aerial image matching")
  Member "proposal flow semantic correspondences from object proposals":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning semantic correspondence exploiting an object level prior")
  Member "warpnet weakly supervised matching for single view reconstruction":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "unsupervised disentanglement of pose appearance and background from images and videos")
  Member "dsac differentiable ransac for camera localization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "visual camera re localization from rgb and rgb d images using dsac")
  Member "spatial transformer networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "transformation based adversarial video prediction on large scale data")

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)