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: "projected subgradient methods for learning sparse gaussians"
  Abstract: Gaussian Markov random fields (GMRFs) are useful in a broad range of applications. In this paper we tackle the problem of learning a sparse GMRF in a high-dimensional space. Our approach uses the l1-norm as a regularization on the inverse covariance matrix. We utilize a novel projected gradient method, which is faster than previous methods in practice and equal to the best performing of these in asymptotic complexity. We also extend the l1-regularized objective to the problem of sparsifying entire blocks within the inverse covariance matrix. Our methods generalize fairly easily to this case, while other methods do not. We demonstrate that our extensions give better generalization performance on two real domains--biological network analysis and a 2D-shape modeling image task.

Connected through hyperedge HE-1 (induced by source paper "gaussian graphical model exploration and selection in high dimension low sample size setting"):
  with members:
    - "sparse inverse covariance selection via alternating linearization methods"

Connected through hyperedge HE-2 (induced by source paper "autoer automated entity resolution using generative modelling"):
  with members:
    - "reuse and adaptation for entity resolution through transfer learning"
    - "snorkel rapid training data creation with weak supervision"
    - "inverse covariance estimation for high dimensional data in linear time and space spectral methods for riccati and sparse models"
    - "crowder crowdsourcing entity resolution"
    - "finding connected components on map reduce in logarithmic rounds"

Connected through hyperedge HE-3 (induced by source paper "a coordinate wise optimization algorithm for sparse inverse covariance selection"):
  with members:
    - "convex optimization for linear query processing under approximate differential privacy"
    - "sparsity constrained nonlinear optimization optimality conditions and algorithms"
    - "sparse approximation via penalty decomposition methods"
    - "sparse inverse covariance selection via alternating linearization methods"

Connected through hyperedge HE-4 (induced by source paper "inverse covariance estimation for high dimensional data in linear time and space spectral methods for riccati and sparse models"):
  with members:
    - "sparse inverse covariance matrix estimation using quadratic approximation"
    - "high dimensional sparse inverse covariance estimation using greedy methods"
    - "sparse inverse covariance selection via alternating linearization methods"

Connected through hyperedge HE-5 (induced by source paper "sparse inverse covariance matrix estimation using quadratic approximation"):
  with members:
    - "sparse inverse covariance selection via alternating linearization methods"

Connected through hyperedge HE-6 (induced by source paper "simultaneous and group sparse multi task learning of gaussian graphical models"):
  with members:
    - "learning structural changes of gaussian graphical models in controlled experiments"
    - "high dimensional sparse inverse covariance estimation using greedy methods"
    - "sparse inverse covariance selection via alternating linearization methods"

Connected through hyperedge HE-7 (induced by source paper "a graphical model formulation of collaborative filtering neighbourhood methods with fast maximum entropy training"):
  with members:
    - "constrained approximate maximum entropy learning of markov random fields"
    - "properties of bethe free energies and message passing in gaussian models"
    - "gaussian belief propagation solver for systems of linear equations"

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)