human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "missing slice recovery for tensors using a low rank model in embedded space"
  Abstract: Let us consider a case where all of the elements in some continuous slices are missing in tensor data. #R##N#In this case, the nuclear-norm and total variation regularization methods usually fail to recover the missing elements. #R##N#The key problem is capturing some delay/shift-invariant structure. #R##N#In this study, we consider a low-rank model in an embedded space of a tensor. #R##N#For this purpose, we extend a delay embedding for a time series to a "multi-way delay-embedding transform" for a tensor, which takes a given incomplete tensor as the input and outputs a higher-order incomplete Hankel tensor. #R##N#The higher-order tensor is then recovered by Tucker-based low-rank tensor factorization. #R##N#Finally, an estimated tensor can be obtained by using the inverse multi-way delay embedding transform of the recovered higher-order tensor. #R##N#Our experiments showed that the proposed method successfully recovered missing slices for some color images and functional magnetic resonance images.

Connected through hyperedge HE-1 (induced by source paper "block hankel tensor arima for multiple short time series forecasting"):
  with members:
    - "manifold modeling in embedded space a perspective for interpreting deep image prior"
    - "model agnostic time series analysis via matrix estimation"
    - "spatio temporal graph convolutional networks a deep learning framework for traffic forecasting"
    - "deepar probabilistic forecasting with autoregressive recurrent networks"
    - "modeling long and short term temporal patterns with deep neural networks"
    - "long term forecasting using higher order tensor rnns"
    - "xgboost a scalable tree boosting system"
    - "tensor networks for dimensionality reduction and large scale optimization part 1 low rank tensor decompositions"

Connected through hyperedge HE-2 (induced by source paper "manifold modeling in embedded space a perspective for interpreting deep image prior"):
  with members:
    - "simultaneous tensor completion and denoising by noise inequality constrained convex optimization"
    - "deep image prior"
    - "efficient low rank tensor ring completion"
    - "transportation analysis of denoising autoencoders a novel method for analyzing deep neural networks"
    - "smooth parafac decomposition for tensor completion"
    - "parallel matrix factorization for low rank tensor completion"
    - "bayesian cp factorization of incomplete tensors with automatic rank determination"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "a comprehensive survey for low rank regularization"):
  with members:
    - "bilinear factor matrix norm minimization for robust pca algorithms and applications"
    - "side information for face completion a robust pca approach"
    - "fast and accurate tensor completion with tensor trains a system identification approach"
    - "discriminative block diagonal representation learning for image recognition"
    - "scalable dense non rigid structure from motion a grassmannian perspective"
    - "multi channel weighted nuclear norm minimization for real color image denoising"
    - "large scale low rank matrix learning with nonconvex regularizers"
    - "side information in robust principal component analysis algorithms and applications"

Connected through hyperedge HE-4 (induced by source paper "low rank tensor completion for multiway visual data"):
  with members:
    - "tractable and scalable schatten quasi norm approximations for rank minimization"
    - "simultaneous tensor completion and denoising by noise inequality constrained convex optimization"
    - "efficient tensor completion for color image and video recovery low rank tensor train"
    - "efficient low rank tensor ring completion"
    - "low rank tensor completion by truncated nuclear norm regularization"
    - "low tubal rank tensor completion using alternating minimization"
    - "scalable algorithms for tractable schatten quasi norm minimization"
    - "reweighted low rank tensor completion and its applications in video recovery"

Question: Please tell me which class the center node 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)