human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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}\...

Includes members:
  Member "recovering low rank matrices from few coefficients in any basis":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "matrix completion with prior subspace information via maximizing correlation")
  Member "strongly convex programming for exact matrix completion and robust principal component analysis":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "the sparse reverse of principal component analysis for fast low rank matrix completion")
  Member "a probabilistic and ripless theory of compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "support recovery for sparse signals with unknown non stationary modulation")
  Member "robust principal component analysis exact recovery of corrupted low rank matrices":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "anomaly detecting and ranking of the cloud computing platform by multi view learning")
  Member "guaranteed rank minimization via singular value projection":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "the importance of phase in complex compressive sensing")
  Member "robust principal component analysis":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "finding the sparsest vectors in a subspace theory algorithms and applications")
  Member "exact matrix completion via convex optimization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "finding the sparsest vectors in a subspace theory algorithms and applications")

Question: Please tell me which class the source paper 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)