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: "recovery guarantee of non negative matrix factorization via alternating updates"
  Abstract: Non-negative matrix factorization is a popular tool for decomposing data into feature and weight matrices under non-negativity constraints. It enjoys practical success but is poorly understood theoretically. This paper proposes an algorithm that alternates between decoding the weights and updating the features, and shows that assuming a generative model of the data, it provably recovers the ground-truth under fairly mild conditions. In particular, its only essential requirement on features is linear independence. Furthermore, the algorithm uses ReLU to exploit the non-negativity for decoding the weights, and thus can tolerate adversarial noise that can potentially be as large as the signal, and can tolerate unbiased noise much larger than the signal. The analysis relies on a carefully designed coupling between two potential functions, which we believe is of independent interest.

Connected through hyperedge HE-1 (induced by source paper "simple and practical algorithms for ell_p norm low rank approximation"):
  with members:
    - "on the complexity of robust pca and l1 norm low rank matrix approximation"
    - "a general and adaptive robust loss function"
    - "low rank matrix approximation in the infinity norm"
    - "algorithms for ell_p low rank approximation"
    - "no spurious local minima in nonconvex low rank problems a unified geometric analysis"
    - "correlation clustering with low rank matrices"
    - "matrix completion has no spurious local minimum"
    - "symmetry saddle points and global geometry of nonconvex matrix factorization"

Connected through hyperedge HE-2 (induced by source paper "recovery guarantees for quadratic tensors with limited observations"):
  with members:
    - "on the power of over parametrization in neural networks with quadratic activation"
    - "exact tensor completion with sum of squares"
    - "an overview of embedding models of entities and relationships for knowledge base completion"
    - "no spurious local minima in nonconvex low rank problems a unified geometric analysis"
    - "riemannian preconditioning for tensor completion"
    - "efficient estimation of word representations in vector space"
    - "tensor decompositions for learning latent variable models"
    - "convex sparse matrix factorizations"

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)