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: "combinatorial selection and least absolute shrinkage via the clash algorithm"
  Abstract: The least absolute shrinkage and selection operator (LASSO) for linear regression exploits the geometric interplay of the $\ell_2$-data error objective and the $\ell_1$-norm constraint to arbitrarily select sparse models. Guiding this uninformed selection process with sparsity models has been precisely the center of attention over the last decade in order to improve learning performance. To this end, we alter the selection process of LASSO to explicitly leverage combinatorial sparsity models (CSMs) via the combinatorial selection and least absolute shrinkage (CLASH) operator. We provide concrete guidelines how to leverage combinatorial constraints within CLASH, and characterize CLASH's guarantees as a function of the set restricted isometry constants of the sensing matrix. Finally, our experimental results show that CLASH can outperform both LASSO and model-based compressive sensing in sparse estimation.

Connected through hyperedge HE-1 (induced by source paper "quantitative recovery conditions for tree based compressed sensing"):
  with members:
    - "structured sparsity discrete and convex approaches"
    - "approximation algorithms for model based compressive sensing"
    - "learning with submodular functions a convex optimization perspective"
    - "k median clustering model based compressive sensing and sparse recovery for earth mover distance"
    - "compressed sensing how sharp is the restricted isometry property"
    - "model based compressive sensing"
    - "phase transitions for greedy sparse approximation algorithms"
    - "subspace pursuit for compressive sensing signal reconstruction"

Connected through hyperedge HE-2 (induced by source paper "learning based compressive subsampling"):
  with members:
    - "a totally unimodular view of structured sparsity"
    - "breaking the coherence barrier a new theory for compressed sensing"
    - "group sparse model selection hardness and relaxations"
    - "an algorithm for variable density sampling with block constrained acquisition"
    - "energy aware adaptive bi lipschitz embeddings"
    - "a tight combinatorial algorithm for submodular maximization subject to a matroid constraint"
    - "on variable density compressive sampling"
    - "model based compressive sensing"

Connected through hyperedge HE-3 (induced by source paper "convex block sparse linear regression with expanders provably"):
  with members:
    - "structured sparsity discrete and convex approaches"
    - "group sparse model selection hardness and relaxations"
    - "on model based rip 1 matrices"
    - "on deterministic sketching and streaming for sparse recovery and norm estimation"
    - "k median clustering model based compressive sensing and sparse recovery for earth mover distance"
    - "model based compressive sensing"
    - "combining geometry and combinatorics a unified approach to sparse signal recovery"

Connected through hyperedge HE-4 (induced by source paper "algorithms for learning sparse additive models with interactions in high dimensions"):
  with members:
    - "learning sparse additive models with interactions in high dimensions"
    - "exact and stable covariance estimation from quadratic sampling via convex programming"
    - "sparse volterra and polynomial regression models recoverability and estimation"
    - "iterative hard thresholding for compressed sensing"

Connected through hyperedge HE-5 (induced by source paper "structured sparsity discrete and convex approaches"):
  with members:
    - "stft phase retrieval uniqueness guarantees and recovery algorithms"
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "group sparse model selection hardness and relaxations"
    - "a novel sparsity and clustering regularization"
    - "on model based rip 1 matrices"
    - "sparse inverse covariance matrix estimation using quadratic approximation"
    - "phase retrieval via matrix completion"
    - "convex approaches to model wavelet sparsity patterns"

Connected through hyperedge HE-6 (induced by source paper "linear inverse problems with norm and sparsity constraints"):
  with members:
    - "model based compressive sensing"
    - "subspace pursuit for compressive sensing signal reconstruction"
    - "iterative hard thresholding for compressed sensing"
    - "combining geometry and combinatorics a unified approach to sparse signal recovery"

Connected through hyperedge HE-7 (induced by source paper "approximation algorithms for model based compressive sensing"):
  with members:
    - "on model based rip 1 matrices"
    - "signal space cosamp for sparse recovery with redundant dictionaries"
    - "sublinear time approximate model based sparse recovery for all"
    - "structured compressed sensing from theory to applications"
    - "optimization with sparsity inducing penalties"
    - "lower bounds for sparse recovery"
    - "model based compressive sensing"
    - "modified cs modifying compressive sensing for problems with partially known support"

Connected through hyperedge HE-8 (induced by source paper "a totally unimodular view of structured sparsity"):
  with members:
    - "provable submodular minimization using wolfe s algorithm"
    - "group sparse model selection hardness and relaxations"
    - "learning with submodular functions a convex optimization perspective"
    - "structured sparsity inducing norms through submodular functions"
    - "is submodularity testable"
    - "model based compressive sensing"

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)