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: "augmented ell_1 and nuclear norm models with a globally linearly convergent algorithm"
  Abstract: This paper studies the long-existing idea of adding a nice smooth function to “smooth” a nondifferentiable objective function in the context of sparse optimization, in particular, the minimization of $\|\mathbf{x}\|_1+\frac{1}{2\alpha}\|\mathbf{x}\|_2^2$, where $\mathbf{x}$ is a vector, as well as the minimization of $\|\mathbf{X}\|_*+\frac{1}{2\alpha}\|\mathbf{X}\|_F^2$, where $\mathbf{X}$ is a matrix and $\|\mathbf{X}\|_*$ and $\|\mathbf{X}\|_F$ are the nuclear and Frobenius norms of $\mathbf{X}$, respectively. We show that they let sparse vectors and low-rank matrices be efficiently recovered. In particular, they enjoy exact and stable recovery guarantees similar to those known for the minimization of $\|\mathbf{x}\|_1$ and $\|\mathbf{X}\|_*$ under the conditions on the sensing operator such as its null-space property, restricted isometry property (RIP), spherical section property, or “RIPless” property. To recover a (nearly) sparse vector $\mathbf{x}^0$, minimizing $\|\mathbf{x}\|_1+\frac{1}{2\alpha}\...

Connected through hyperedge HE-1 (induced by source paper "joint activity detection and channel estimation for iot networks phase transition and computation estimation tradeoff"):
  with members:
    - "energy sustainable iot with individual qos constraints through miso swipt multicasting"
    - "sparse representation for wireless communications"
    - "sparse activity detection for massive connectivity"
    - "massive connectivity with massive mimo part ii achievable rate characterization"
    - "sparse signal processing for grant free massive connectivity a future paradigm for random access protocols in the internet of things"
    - "massive connectivity with massive mimo part i device activity detection and channel estimation"
    - "compressive channel estimation and multi user detection in c ran"
    - "a random access protocol for pilot allocation in crowded massive mimo systems"

Connected through hyperedge HE-2 (induced by source paper "robust compressed sensing under matrix uncertainties"):
  with members:
    - "multi structural signal recovery for biomedical compressive sensing"
    - "multi sparse signal recovery for compressive sensing"
    - "the performance of orthogonal multi matching pursuit under rip"
    - "anti sampling distortion compressive wideband spectrum sensing for cognitive radio"
    - "enhanced compressive wideband frequency spectrum sensing for dynamic spectrum access"

Connected through hyperedge HE-3 (induced by source paper "robust sparse signal recovery for compressed sensing with sampling and dictionary uncertainties"):
  with members:
    - "multi structural signal recovery for biomedical compressive sensing"
    - "multi sparse signal recovery for compressive sensing"
    - "anti sampling distortion compressive wideband spectrum sensing for cognitive radio"
    - "enhanced compressive wideband frequency spectrum sensing for dynamic spectrum access"

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)