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 "manifold modeling in embedded space a perspective for interpreting deep image prior"):
  Abstract: Deep image prior (DIP), which utilizes a deep convolutional network (ConvNet) structure itself as an image prior, has attractive attentions in computer vision community. It empirically showed that the effectiveness of ConvNet structure in various image restoration applications. However, why the DIP works so well is still in black box, and why ConvNet is essential for images is not very clear. #R##N#In this study, we tackle this question by considering the convolution divided into "embedding" and "transformation", and proposing a simple, but essential, modeling approach of images/tensors related with dynamical system or self-similarity. The proposed approach named as manifold modeling in embedded space (MMES) can be implemented by using a denoising-auto-encoder in combination with multiway delay-embedding transform. In spite of its simplicity, the image/tensor completion and super-resolution results of MMES were very similar even competitive with DIP in our experiments, and these results would help us for reinterpreting/characterizing the DIP from a perspective of "smooth patch-manifold prior".

Includes members:
  Member "simultaneous tensor completion and denoising by noise inequality constrained convex optimization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "low rank tensor completion for multiway visual data")
  Member "missing slice recovery for tensors using a low rank model in embedded space":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "block hankel tensor arima for multiple short time series forecasting")
  Member "deep image prior":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "semantic pyramid for image generation")
  Member "efficient low rank tensor ring completion":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "low rank tensor grid for image completion")
  Member "transportation analysis of denoising autoencoders a novel method for analyzing deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "transport analysis of infinitely deep neural network")
  Member "smooth parafac decomposition for tensor completion":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "provable model for tensor ring completion")
  Member "parallel matrix factorization for low rank tensor completion":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "low rank tensor grid for image completion")
  Member "bayesian cp factorization of incomplete tensors with automatic rank determination":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "low rank tensor grid for image completion")

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)