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: "block coordinate descent for sparse nmf"
  Abstract: Nonnegative matrix factorization (NMF) has become a ubiquitous tool for data analysis. An important variant is the sparse NMF problem which arises when we explicitly require the learnt features to be sparse. A natural measure of sparsity is the L$_0$ norm, however its optimization is NP-hard. Mixed norms, such as L$_1$/L$_2$ measure, have been shown to model sparsity robustly, based on intuitive attributes that such measures need to satisfy. This is in contrast to computationally cheaper alternatives such as the plain L$_1$ norm. However, present algorithms designed for optimizing the mixed norm L$_1$/L$_2$ are slow and other formulations for sparse NMF have been proposed such as those based on L$_1$ and L$_0$ norms. Our proposed algorithm allows us to solve the mixed norm sparsity constraints while not sacrificing computation time. We present experimental evidence on real-world datasets that shows our new algorithm performs an order of magnitude faster compared to the current state-of-the-art solvers optimizing the mixed norm and is suitable for large-scale datasets.

Connected through hyperedge HE-1 (induced by source paper "grouped sparse projection"):
  with members:
    - "deephoyer learning sparser neural network with differentiable scale invariant sparsity measures"
    - "learning sparse networks using targeted dropout"
    - "fastgrnn a fast accurate stable and tiny kilobyte sized gated recurrent neural network"
    - "importance estimation for neural network pruning"
    - "the state of sparsity in deep neural networks"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "rethinking the value of network pruning"
    - "inductive bias of deep convolutional networks through pooling geometry"

Connected through hyperedge HE-2 (induced by source paper "efficient dictionary learning with sparseness enforcing projections"):
  with members:
    - "sparse activity and sparse connectivity in supervised learning"
    - "separable dictionary learning"
    - "feature grouping from spatially constrained multiplicative interaction"
    - "estimating unknown sparsity in compressed sensing"
    - "learning sparse representations of depth"
    - "image deblurring and super resolution by adaptive sparse domain selection and adaptive regularization"
    - "comparing measures of sparsity"
    - "non negative matrix factorization with sparseness constraints"

Connected through hyperedge HE-3 (induced by source paper "computational intractability of dictionary learning for sparse representation"):
  with members:
    - "time delay estimation from low rate samples a union of subspaces approach"
    - "supervised dictionary learning"
    - "non negative matrix factorization with sparseness constraints"

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)