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: "compressed sensing with side information geometrical interpretation and performance bounds"
  Abstract: We address the problem of Compressed Sensing (CS) with side information. Namely, when reconstructing a target CS signal, we assume access to a similar signal. This additional knowledge, the side information, is integrated into CS via L1-L1 and L1-L2 minimization. We then provide lower bounds on the number of measurements that these problems require for successful reconstruction of the target signal. If the side information has good quality, the number of measurements is significantly reduced via L1-L1 minimization, but not so much via L1-L2 minimization. We provide geometrical interpretations and experimental results illustrating our findings.

Connected through hyperedge HE-1 (induced by source paper "multimodal image super resolution via deep unfolding with side information"):
  with members:
    - "deep back projection networks for super resolution"
    - "image super resolution via dual state recurrent networks"
    - "amp inspired deep networks for sparse linear inverse problems"
    - "multimodal image super resolution via joint sparse representations induced by coupled dictionaries"
    - "deep laplacian pyramid networks for fast and accurate super resolution"
    - "enhanced deep residual networks for single image super resolution"
    - "maximal sparsity with deep networks"
    - "accurate image super resolution using very deep convolutional networks"

Connected through hyperedge HE-2 (induced by source paper "side information in robust principal component analysis algorithms and applications"):
  with members:
    - "fast robust pca on graphs"
    - "partial sum minimization of singular values in robust pca algorithm and applications"
    - "robust principal component analysis on graphs"
    - "justification of logarithmic loss via the benefit of side information"
    - "deception with side information in biometric authentication systems"
    - "adaptive rate compressive sensing using side information"
    - "robust recovery of subspace structures by low rank representation"
    - "stable principal component pursuit"

Connected through hyperedge HE-3 (induced by source paper "incorporating prior information in compressive online robust principal component analysis"):
  with members:
    - "recursive recovery of sparse signal sequences from compressive measurements a review"
    - "sparse signal reconstruction with multiple side information using adaptive weights for multiview sources"
    - "an online algorithm for separating sparse and low dimensional signal sequences from their sum"
    - "living on the edge phase transitions in convex programs with random data"
    - "recursive robust pca or recursive sparse recovery in large but structured noise"
    - "compressive principal component pursuit"
    - "support predicted modified cs for recursive robust principal components pursuit"
    - "modified cs modifying compressive sensing for problems with partially known support"

Connected through hyperedge HE-4 (induced by source paper "multi modal dictionary learning for image separation with application in art investigation"):
  with members:
    - "classification and reconstruction of high dimensional signals from low dimensional features in the presence of side information"
    - "x ray image separation via coupled dictionary learning"
    - "compressed sensing with prior information optimal strategies geometry and bounds"
    - "new algorithms for learning incoherent and overcomplete dictionaries"
    - "bayesian crack detection in ultra high resolution multimodal images of paintings"
    - "exact recovery of sparsely used dictionaries"
    - "modified cs modifying compressive sensing for problems with partially known support"

Connected through hyperedge HE-5 (induced by source paper "performance bounds for sparse signal reconstruction with multiple side information"):
  with members:
    - "recursive recovery of sparse signal sequences from compressive measurements a review"
    - "adaptive rate compressive sensing using side information"
    - "an online algorithm for separating sparse and low dimensional signal sequences from their sum"
    - "robust pca with partial subspace knowledge"
    - "living on the edge phase transitions in convex programs with random data"
    - "time invariant error bounds for modified cs based sparse signal sequence recovery"
    - "regularized modified bpdn for noisy sparse reconstruction with partial erroneous support and signal value knowledge"
    - "recursive robust pca or recursive sparse recovery in large but structured noise"

Connected through hyperedge HE-6 (induced by source paper "distributed coding of multiview sparse sources with joint recovery"):
  with members:
    - "sparse signal reconstruction with multiple side information using adaptive weights for multiview sources"
    - "operational rate distortion performance of single source and distributed compressed sensing"

Connected through hyperedge HE-7 (induced by source paper "x ray image separation via coupled dictionary learning"):
  with members:
    - "compressed sensing with prior information optimal strategies geometry and bounds"
    - "modified cs modifying compressive sensing for problems with partially known support"

Connected through hyperedge HE-8 (induced by source paper "sparse signal reconstruction with multiple side information using adaptive weights for multiview sources"):
  with members:
    - "adaptive rate compressive sensing using side information"

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.IT(Information Theory)