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 "stereoscopic neural style transfer"):
  Abstract: This paper presents the first attempt at stereoscopic neural style transfer, which responds to the emerging demand for 3D movies or AR/VR. We start with a careful examination of applying existing monocular style transfer methods to left and right views of stereoscopic images separately. This reveals that the original disparity consistency cannot be well preserved in the final stylization results, which causes 3D fatigue to the viewers. To address this issue, we incorporate a new disparity loss into the widely adopted style loss function by enforcing the bidirectional disparity constraint in non-occluded regions. For a practical real-time solution, we propose the first feed-forward network by jointly training a stylization sub-network and a disparity sub-network, and integrate them in a feature level middle domain. Our disparity sub-network is also the first end-to-end network for simultaneous bidirectional disparity and occlusion mask estimation. Finally, our network is effectively extended to stereoscopic videos, by considering both temporal coherence and disparity consistency. We will show that the proposed method clearly outperforms the baseline algorithms both quantitatively and qualitatively.

Includes members:
  Member "artistic style transfer for videos and spherical images":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a content transformation block for image style transfer")
  Member "continuous 3d label stereo matching using local expansion moves":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep depth prior for multi view stereo")
  Member "towards open set identity preserving face synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "fine grained image to image transformation towards visual recognition")
  Member "learning selfie friendly abstraction from artistic style images":
    also connected through other hyperedges:
      (none)
  Member "avatar net multi scale zero shot style transfer by feature decoration":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "regularized adaptation for stable and efficient continuous level learning on image processing networks")
  Member "characterizing and improving stability in neural style transfer":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "efficient semantic video segmentation with per frame inference")
  Member "revisiting deep intrinsic image decompositions":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "intrinsic image transformation via scale space decomposition")
  Member "a generic deep architecture for single image reflection removal and image smoothing":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "cooperative semantic segmentation and image restoration in adverse environmental conditions")

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)