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: "complexity of unconstrained l_2 l_p minimization"
  Abstract: We consider the unconstrained $L_2$-$L_p$ minimization: find a minimizer of $\|Ax-b\|^2_2+\lambda \|x\|^p_p$ for given $A \in R^{m\times n}$, $b\in R^m$ and parameters $\lambda>0$, $p\in [0,1)$. This problem has been studied extensively in variable selection and sparse least squares fitting for high dimensional data. Theoretical results show that the minimizers of the $L_2$-$L_p$ problem have various attractive features due to the concavity and non-Lipschitzian property of the regularization function $\|\cdot\|^p_p$. In this paper, we show that the $L_q$-$L_p$ minimization problem is strongly NP-hard for any $p\in [0,1)$ and $q\ge 1$, including its smoothed version. On the other hand, we show that, by choosing parameters $(p,\lambda)$ carefully, a minimizer, global or local, will have certain desired sparsity. We believe that these results provide new theoretical insights to the studies and applications of the concave regularized optimization problems.

Connected through hyperedge HE-1 (induced by source paper "nonconvex regularization based sparse and low rank recovery in signal processing statistics and machine learning"):
  with members:
    - "nonconvex optimization meets low rank matrix factorization an overview"
    - "efficient outlier removal for large scale global structure from motion"
    - "nonconvex regularization based sparse recovery and demixing with application to color image inpainting"
    - "deterministic approximate methods for maximum consensus robust fitting"
    - "unified scalable equivalent formulations for schatten quasi norms"
    - "enhanced low rank matrix approximation"
    - "scalable algorithms for tractable schatten quasi norm minimization"
    - "matrix completion has no spurious local minimum"

Connected through hyperedge HE-2 (induced by source paper "visual data denoising with a unified schatten p norm and lq norm regularized principal component pursuit"):
  with members:
    - "generalized nonconvex nonsmooth low rank minimization"
    - "robust recovery of subspace structures by low rank representation"
    - "stable principal component pursuit"
    - "robust principal component analysis"

Connected through hyperedge HE-3 (induced by source paper "joint representation classification for collective face recognition"):
  with members:
    - "image set based collaborative representation for face recognition"

Connected through hyperedge HE-4 (induced by source paper "on the conditions of sparse parameter estimation via log sum penalty regularization"):
  with members:
    - "a general iterative shrinkage and thresholding algorithm for non convex regularized optimization problems"
    - "a probabilistic and ripless theory of compressed sensing"
    - "sparse recovery with orthogonal matching pursuit under rip"
    - "new bounds for restricted isometry constants"
    - "iterative hard thresholding for compressed sensing"

Connected through hyperedge HE-5 (induced by source paper "relaxed sparse eigenvalue conditions for sparse estimation via non convex regularized regression"):
  with members:
    - "a general iterative shrinkage and thresholding algorithm for non convex regularized optimization problems"
    - "a probabilistic and ripless theory of compressed sensing"
    - "sparse recovery with orthogonal matching pursuit under rip"
    - "structured sparsity inducing norms through submodular functions"

Connected through hyperedge HE-6 (induced by source paper "joint power and admission control non convex l_q approximation and an effective polynomial time deflation approach"):
  with members:
    - "a decentralized method for joint admission control and beamforming in coordinated multicell downlink"
    - "on the complexity of joint subcarrier and power allocation for multi user ofdma systems"

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.CC(Computational Complexity)