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: "consistency of the group lasso and multiple kernel learning"
  Abstract: We consider the least-square regression problem with regularization by a block 1-norm, i.e., a sum of Euclidean norms over spaces of dimensions larger than one. This problem, referred to as the group Lasso, extends the usual regularization by the 1-norm where all spaces have dimension one, where it is commonly referred to as the Lasso. In this paper, we study the asymptotic model consistency of the group Lasso. We derive necessary and sufficient conditions for the consistency of group Lasso under practical assumptions, such as model misspecification. When the linear predictors and Euclidean norms are replaced by functions and reproducing kernel Hilbert norms, the problem is usually referred to as multiple kernel learning and is commonly used for learning from heterogeneous data sources and for non linear variable selection. Using tools from functional analysis, and in particular covariance operators, we extend the consistency results to this infinite dimensional case and also propose an adaptive scheme to obtain a consistent model estimate, even when the necessary condition required for the non adaptive scheme is not satisfied.

Connected through hyperedge HE-1 (induced by source paper "joint user selection and precoding in multiuser mimo systems via group lasso"):
  with members:
    - "secure regularized zero forcing for multiuser mimome channels"
    - "precoding via approximate message passing with instantaneous signal constraints"
    - "on robustness of massive mimo systems against passive eavesdropping under antenna selection"
    - "glse precoders for massive mimo systems analysis and applications"
    - "theoretical bounds on map estimation in distributed sensing networks"
    - "nonlinear precoders for massive mimo systems with general constraints"
    - "asymptotics of nonlinear lse precoders with applications to transmit antenna selection"
    - "generalized approximate message passing for estimation with random linear mixing"

Connected through hyperedge HE-2 (induced by source paper "physics informed topology learning in networks of linear dynamical systems"):
  with members:
    - "data driven identification of a thermal network in multi zone building"
    - "learning the exact topology of undirected consensus networks"
    - "network inference from consensus dynamics"
    - "network topology inference from spectral templates"
    - "graph learning from data under laplacian and structural constraints"
    - "topology identification of directed dynamical networks via power spectral analysis"
    - "directed information graphs"

Connected through hyperedge HE-3 (induced by source paper "large scale nonlinear variable selection via kernel random features"):
  with members:
    - "group sparse additive models"
    - "high dimensional non linear variable selection through hierarchical kernel learning"
    - "supervised feature selection via dependence estimation"

Connected through hyperedge HE-4 (induced by source paper "sparse learning for variable selection with structures and nonlinearities"):
  with members:
    - "forecasting and granger modelling with non linear dynamical dependencies"
    - "optimization with sparsity inducing penalties"

Connected through hyperedge HE-5 (induced by source paper "improved support recovery guarantees for the group lasso with applications to structural health monitoring"):
  with members:
    - "corrupted sensing novel guarantees for separating structured signals"
    - "on block coherence of frames"
    - "the degrees of freedom of the group lasso for a general design"
    - "subspace methods for joint sparse recovery"
    - "measurement bounds for sparse signal ensembles via graphical models"
    - "near oracle performance of greedy block sparse estimation techniques from noisy measurements"
    - "recovery of block sparse representations from noisy observations via orthogonal matching pursuit"
    - "structured compressed sensing from theory to applications"

Connected through hyperedge HE-6 (induced by source paper "feature selection a data perspective"):
  with members:
    - "gradient boosted feature selection"
    - "challenges of feature selection for big data analytics"
    - "online feature selection with group structure analysis"
    - "unsupervised feature selection with adaptive structure learning"
    - "attentional neural network feature selection using cognitive feedback"
    - "generalized fisher score for feature selection"
    - "towards ultrahigh dimensional feature selection for big data"
    - "simple and deterministic matrix sketching"

Connected through hyperedge HE-7 (induced by source paper "multiple kernel learning and automatic subspace relevance determination for high dimensional neuroimaging data"):
  with members:
    - "sparse 3d convolutional neural networks"
    - "deepstereo learning to predict new views from the world s imagery"
    - "predicting alzheimer s disease a neuroimaging study with 3d convolutional neural networks"
    - "going deeper with convolutions"
    - "learning to generate chairs tables and cars with convolutional networks"
    - "variable noise and dimensionality reduction for sparse gaussian processes"
    - "l2 regularization for learning kernels"
    - "building high level features using large scale unsupervised learning"

Connected through hyperedge HE-8 (induced by source paper "on the sample complexity of graphical model selection for non stationary processes"):
  with members:
    - "network lasso clustering and optimization in large graphs"
    - "the distortion rate function of cyclostationary gaussian processes"
    - "noncoherent capacity of underspread fading channels"
    - "information theoretic limits of selecting binary graphical models in high dimensions"

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)