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: "compressed nonnegative matrix factorization is fast and accurate"
  Abstract: Nonnegative matrix factorization (NMF) has an established reputation as a useful data analysis technique in numerous applications. However, its usage in practical situations is undergoing challenges in recent years. The fundamental factor to this is the increasingly growing size of the datasets available and needed in the information sciences. To address this, in this work we propose to use structured random compression, that is, random projections that exploit the data structure, for two NMF variants: classical and separable. In separable NMF (SNMF), the left factors are a subset of the columns of the input matrix. We present suitable formulations for each problem, dealing with different representative algorithms within each one. We show that the resulting compressed techniques are faster than their uncompressed variants, vastly reduce memory demands, and do not encompass any significant deterioration in performance. The proposed structured random projections for SNMF allow to deal with arbitrarily shaped large matrices, beyond the standard limit of tall-and-skinny matrices, granting access to very efficient computations in this general setting. We accompany the algorithmic presentation with theoretical foundations and numerous and diverse examples, showing the suitability of the proposed approaches. Our implementations are publicly available.

Connected through hyperedge HE-1 (induced by source paper "compressed factorization fast and accurate low rank factorization of compressively sensed data"):
  with members:
    - "a practical randomized cp tensor decomposition"
    - "orthogonalized als a theoretically principled tensor decomposition algorithm for practical use"
    - "stable robust and super fast reconstruction of tensors using multi way projections"
    - "guaranteed non orthogonal tensor decomposition via alternating rank 1 updates"
    - "low rank approximation and regression in input sparsity time"
    - "learning topic models going beyond svd"
    - "exact recovery of sparsely used dictionaries"
    - "combining geometry and combinatorics a unified approach to sparse signal recovery"

Connected through hyperedge HE-2 (induced by source paper "efficient preconditioning for noisy separable nmfs by successive projection based low rank approximations"):
  with members:
    - "spectral unmixing via data guided sparsity"
    - "a practical algorithm for topic modeling with provable guarantees"
    - "learning topic models going beyond svd"
    - "computing a nonnegative matrix factorization provably"
    - "matrix completion from noisy entries"
    - "matrix completion with noise"
    - "on the complexity of nonnegative matrix factorization"

Connected through hyperedge HE-3 (induced by source paper "clustering is semidefinitely not that hard nonnegative sdp for manifold disentangling"):
  with members:
    - "on the tightness of an sdp relaxation of k means"
    - "on semidefinite relaxations for the block model"
    - "a biclustering framework for consensus problems"
    - "an alternating direction algorithm for matrix completion with nonnegative factors"
    - "algorithms for learning kernels based on centered alignment"
    - "a new embedding quality assessment method for manifold learning"

Connected through hyperedge HE-4 (induced by source paper "an efficient algorithm for non negative matrix factorization with random projections"):
  with members:
    - "a high performance parallel algorithm for nonnegative matrix factorization"

Connected through hyperedge HE-5 (induced by source paper "how to fake multiply by a gaussian matrix"):
  with members:
    - "sketching as a tool for numerical linear algebra"
    - "scalable methods for nonnegative matrix factorizations of near separable tall and skinny matrices"
    - "fastfood approximate kernel expansions in loglinear time"
    - "random projections for linear support vector machines"
    - "low rank approximation and regression in input sparsity time"
    - "computing a nonnegative matrix factorization provably"
    - "on the complexity of nonnegative matrix factorization"

Connected through hyperedge HE-6 (induced by source paper "nonnegative matrix underapproximation for robust multiple model fitting"):
  with members:
    - "two points fundamental matrix"
    - "fast l1 nmf for multiple parametric model estimation"
    - "a biclustering framework for consensus problems"
    - "an alternating direction algorithm for matrix completion with nonnegative factors"
    - "on the complexity of nonnegative matrix factorization"

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)