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 prior information optimal strategies geometry and bounds"
  Abstract: We address the problem of compressed sensing (CS) with prior information: reconstruct a target CS signal with the aid of a similar signal that is known beforehand, our prior information. We integrate the additional knowledge of the similar signal into CS via L1-L1 and L1-L2 minimization. We then establish bounds on the number of measurements required by these problems to successfully reconstruct the original signal. Our bounds and geometrical interpretations reveal that if the prior information has good enough quality, L1-L1 minimization improves the performance of CS dramatically. In contrast, L1-L2 minimization has a performance very similar to classical CS and brings no significant benefits. All our findings are illustrated with experimental results.

Connected through hyperedge HE-1 (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 side information geometrical interpretation and performance 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-2 (induced by source paper "classification and reconstruction of high dimensional signals from low dimensional features in the presence of side information"):
  with members:
    - "reconstruction of signals drawn from a gaussian mixture via noisy compressive measurements"
    - "multi terminal probabilistic compressed sensing"
    - "compressive classification of a mixture of gaussians analysis designs and geometrical interpretation"
    - "breaking the coherence barrier a new theory for compressed sensing"
    - "task driven adaptive statistical compressive sensing of gaussian mixture models"
    - "exact recovery conditions for sparse representations with partial support information"
    - "approximate message passing based compressed sensing reconstruction with generalized elastic net prior"
    - "communications inspired projection design with application to compressive sensing"

Connected through hyperedge HE-3 (induced by source paper "weighted ell_1 minimization for sparse recovery under arbitrary prior information"):
  with members:
    - "recovery analysis for weighted ell_1 minimization using a null space property"
    - "exact recovery for sparse signal via weighted l_1 minimization"
    - "on the theorem of uniform recovery of random sampling matrices"
    - "weighted ell_1 minimization with multiple weighting sets"
    - "robust 1 bit compressive sensing via binary stable embeddings of sparse vectors"
    - "recovering compressively sampled signals using partial support information"
    - "model based compressive sensing"
    - "modified cs modifying compressive sensing for problems with partially known support"

Connected through hyperedge HE-4 (induced by source paper "total variation regularized tensor rpca for background subtraction from compressive measurements"):
  with members:
    - "compressive sensing of sparse tensors"
    - "an online algorithm for separating sparse and low dimensional signal sequences from their sum"
    - "local convergence of an algorithm for subspace identification from partial data"
    - "fast and efficient compressive sensing using structurally random matrices"
    - "surveillance video processing using compressive sensing"
    - "moving object detection by detecting contiguous outliers in the low rank representation"
    - "online robust subspace tracking from partial information"
    - "online identification and tracking of subspaces from highly incomplete information"

Connected through hyperedge HE-5 (induced by source paper "x ray image separation via coupled dictionary learning"):
  with members:
    - "compressed sensing with side information geometrical interpretation and performance bounds"
    - "modified cs modifying compressive sensing for problems with partially known support"

Connected through hyperedge HE-6 (induced by source paper "performance analysis of joint sparse recovery from multiple measurements and prior information via convex optimization"):
  with members:
    - "photoplethysmography based heart rate monitoring in physical activities via joint sparse spectrum reconstruction"
    - "living on the edge phase transitions in convex programs with random data"

Connected through hyperedge HE-7 (induced by source paper "approximate message passing based compressed sensing reconstruction with generalized elastic net prior"):
  with members:
    - "classification and reconstruction of high dimensional signals from low dimensional features in the presence of side information"
    - "compressive imaging via approximate message passing with image denoising"
    - "binary linear classification and feature selection via generalized approximate message passing"
    - "an empirical bayes approach to recovering linearly constrained non negative sparse signals"
    - "on the convergence of approximate message passing with arbitrary matrices"
    - "from denoising to compressed sensing"
    - "efficient high dimensional inference in the multiple measurement vector problem"
    - "parameterless optimal approximate message passing"

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)