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: "a dirty model for multiple sparse regression"
  Abstract: Sparse linear regression -- finding an unknown vector from linear measurements -- is now known to be possible with fewer samples than variables, via methods like the LASSO. We consider the multiple sparse linear regression problem, where several related vectors -- with partially shared support sets -- have to be recovered. A natural question in this setting is whether one can use the sharing to further decrease the overall number of samples required. A line of recent research has studied the use of \ell_1/\ell_q norm block-regularizations with q>1 for such problems; however these could actually perform worse in sample complexity -- vis a vis solving each problem separately ignoring sharing -- depending on the level of sharing. #R##N#We present a new method for multiple sparse linear regression that can leverage support and parameter overlap when it exists, but not pay a penalty when it does not. A very simple idea: we decompose the parameters into two components and regularize these differently. We show both theoretically and empirically, our method strictly and noticeably outperforms both \ell_1 or \ell_1/\ell_q methods, over the entire range of possible overlaps (except at boundary cases, where we match the best method). We also provide theoretical guarantees that the method performs well under high-dimensional scaling.

Connected through hyperedge HE-1 (induced by source paper "channel estimation and hybrid precoding for distributed phased arrays based mimo wireless communications"):
  with members:
    - "modeling and analyzing millimeter wave cellular systems"
    - "framework of channel estimation for hybrid analog and digital processing enabled massive mimo communications"
    - "5g cellular user equipment from theory to practical hardware design"
    - "alternating minimization algorithms for hybrid precoding in millimeter wave mimo systems"
    - "an overview of signal processing techniques for millimeter wave mimo systems"
    - "codebook design for millimeter wave channel estimation with hybrid precoding structure"
    - "the world s first real time testbed for massive mimo design implementation and validation"
    - "an overview of transmission theory and techniques of large scale antenna systems for 5g wireless communications"

Connected through hyperedge HE-2 (induced by source paper "on better exploring and exploiting task relationships in multitask learning joint model and feature learning"):
  with members:
    - "pareto path multitask multiple kernel learning"
    - "sparse coding for multitask and transfer learning"
    - "a model of inductive bias learning"

Connected through hyperedge HE-3 (induced by source paper "multi stage multi task feature learning via adaptive threshold"):
  with members:
    - "randomized structural sparsity via constrained block subsampling for improved sensitivity of discriminative voxel identification"

Connected through hyperedge HE-4 (induced by source paper "the achievable performance of convex demixing"):
  with members:
    - "corrupted sensing novel guarantees for separating structured signals"
    - "a framework to characterize performance of lasso algorithms"
    - "compressive principal component pursuit"
    - "probabilistic recovery guarantees for sparsely corrupted signals"
    - "compressed sensing and matrix completion with constant proportion of corruptions"
    - "clustering partially observed graphs via convex optimization"
    - "low rank matrix recovery from errors and erasures"
    - "recovery of sparsely corrupted signals"

Connected through hyperedge HE-5 (induced by source paper "multi target regression via input space expansion treating targets as inputs"):
  with members:
    - "learning internal representations"
    - "rectifying classifier chains for multi label classification"
    - "multi target regression via random linear target combinations"
    - "efficient monte carlo methods for multi dimensional learning with classifier chains"
    - "the landmark selection method for multiple output prediction"
    - "multi label classification methods for multi target regression"
    - "maximum margin output coding"
    - "multi label prediction via compressed 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.LG(Machine Learning)