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: "optimized projections for compressed sensing via rank constrained nearest correlation matrix"
  Abstract: Optimizing the acquisition matrix is useful for compressed sensing of signals that are sparse in overcomplete dictionaries, because the acquisition matrix can be adapted to the particular correlations of the dictionary atoms. In this paper a novel formulation of the optimization problem is proposed, in the form of a rank-constrained nearest correlation matrix problem. Furthermore, improvements for three existing optimization algorithms are introduced, which are shown to be particular instances of the proposed formulation. Simulation results show notable improvements and superior robustness in sparse signal recovery.

Connected through hyperedge HE-1 (induced by source paper "compressed sensing with probability based prior information"):
  with members:
    - "subspace orbit randomized decomposition for low rank matrix approximations"
    - "compressed sensing mri reconstruction using a generative adversarial network with a cyclic loss"
    - "masked signal decomposition using subspace representation and its applications"
    - "on collaborative compressive sensing systems the framework design and algorithm"
    - "widely linear precoding for large scale mimo with iqi algorithms and performance analysis"
    - "joint sensing matrix and sparsifying dictionary optimization for tensor compressive sensing"
    - "iterative detection and decoding algorithms for mimo systems in block fading channels using ldpc codes"
    - "robust adaptive beamforming based on low rank and cross correlation techniques"

Connected through hyperedge HE-2 (induced by source paper "joint sensing matrix and sparsifying dictionary optimization for tensor compressive sensing"):
  with members:
    - "compressive sensing of sparse tensors"
    - "separable cosparse analysis operator learning"
    - "projection design for statistical compressive sensing a tight frame based approach"
    - "simultaneously structured models with application to sparse and low rank matrices"
    - "compressed sensing of simultaneous low rank and joint sparse matrices"
    - "simultaneous codeword optimization simco for dictionary update and learning"
    - "iterative hard thresholding for compressed sensing"
    - "exact matrix completion via convex optimization"

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)