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: "bolasso model consistent lasso estimation through the bootstrap"
  Abstract: We consider the least-square linear regression problem with regularization by the l1-norm, a problem usually referred to as the Lasso. In this paper, we present a detailed asymptotic analysis of model consistency of the Lasso. For various decays of the regularization parameter, we compute asymptotic equivalents of the probability of correct model selection (i.e., variable selection). For a specific rate decay, we show that the Lasso selects all the variables that should enter the model with probability tending to one exponentially fast, while it selects all other variables with strictly positive probability. We show that this property implies that if we run the Lasso for several bootstrapped replications of a given sample, then intersecting the supports of the Lasso bootstrap estimates leads to consistent model selection. This novel variable selection algorithm, referred to as the Bolasso, is compared favorably to other linear regression methods on synthetic data and datasets from the UCI machine learning repository.

Connected through hyperedge HE-1 (induced by source paper "flu detector estimating influenza like illness rates from online user generated content"):
  with members:
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-2 (induced by source paper "detecting events and patterns in large scale user generated textual streams with statistical learning methods"):
  with members:
    - "on voting intentions inference from twitter content a case study on uk 2010 general election"
    - "an analysis of twitter messages in the 2011 tohoku earthquake"
    - "omg u got flu analysis of shared health messages for bio surveillance"
    - "twitter mood predicts the stock market"
    - "detecting influenza outbreaks by analyzing twitter messages"
    - "social networks that matter twitter under the microscope"

Connected through hyperedge HE-3 (induced by source paper "small sample brain mapping sparse recovery on spatially correlated designs with randomization and clustering"):
  with members:
    - "a supervised clustering approach for fmri based inference of brain states"
    - "trace lasso a trace norm regularization for correlated designs"
    - "self concordant analysis for logistic regression"

Connected through hyperedge HE-4 (induced by source paper "trace lasso a trace norm regularization for correlated designs"):
  with members:
    - "consistency of trace norm minimization"
    - "consistency of the group lasso and multiple kernel learning"

Connected through hyperedge HE-5 (induced by source paper "self concordant analysis for logistic regression"):
  with members:
    - "model selection through sparse maximum likelihood estimation"
    - "consistency of the group lasso and multiple kernel learning"

Connected through hyperedge HE-6 (induced by source paper "model consistent sparse estimation through the bootstrap"):
  with members:
    - "exploring large feature spaces with hierarchical multiple kernel learning"
    - "consistency of the group lasso and multiple kernel learning"

Connected through hyperedge HE-7 (induced by source paper "consistency of the group lasso and multiple kernel learning"):
  with members:
    - "consistency of trace norm minimization"

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)