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: "learning with the weighted trace norm under arbitrary sampling distributions"
  Abstract: We provide rigorous guarantees on learning with the weighted trace-norm under arbitrary sampling distributions. We show that the standard weighted trace-norm might fail when the sampling distribution is not a product distribution (i.e. when row and column indexes are not selected independently), present a corrected variant for which we establish strong learning guarantees, and demonstrate that it works better in practice. We provide guarantees when weighting by either the true or empirical sampling distribution, and suggest that even if the true distribution is known (or is uniform), weighting by the empirical distribution may be beneficial.

Connected through hyperedge HE-1 (induced by source paper "matrix completion with prior subspace information via maximizing correlation"):
  with members:
    - "optimal weighted low rank matrix recovery with subspace prior information"
    - "recovery of structured signals with prior information via maximizing correlation"
    - "weighted matrix completion and recovery with prior subspace information"
    - "incoherence optimal matrix completion"
    - "provable inductive matrix completion"
    - "recovering low rank matrices from few coefficients in any basis"
    - "clustering partially observed graphs via convex optimization"
    - "restricted strong convexity and weighted matrix completion optimal bounds with noise"

Connected through hyperedge HE-2 (induced by source paper "low rank matrix completion in a general non orthogonal basis"):
  with members:
    - "exact reconstruction of euclidean distance geometry problem using low rank matrix completion"
    - "fast and provable algorithms for spectrally sparse signal reconstruction via low rank hankel matrix completion"
    - "matrix completion on graphs"
    - "note on sampling without replacing from a finite collection of matrices"
    - "the power of convex relaxation near optimal matrix completion"
    - "a simpler approach to matrix completion"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-3 (induced by source paper "matrix completion via max norm constrained optimization"):
  with members:
    - "recovering low rank matrices from few coefficients in any basis"
    - "concentration based guarantees for low rank matrix reconstruction"
    - "optimistic rates for learning with a smooth loss"
    - "tight oracle bounds for low rank matrix recovery from a minimal number of random measurements"
    - "restricted strong convexity and weighted matrix completion optimal bounds with noise"
    - "collaborative filtering in a non uniform world learning with the weighted trace norm"
    - "uniqueness of low rank matrix completion by rigidity theory"
    - "matrix completion from noisy entries"

Connected through hyperedge HE-4 (induced by source paper "adjusting leverage scores by row weighting a practical approach to coherent matrix completion"):
  with members:
    - "sketching as a tool for numerical linear algebra"
    - "uniform sampling for matrix approximation"
    - "nomad non locking stochastic multi machine algorithm for asynchronous and decentralized matrix completion"
    - "low rank approximation and regression in input sparsity time"
    - "near optimal column based matrix reconstruction"
    - "the power of convex relaxation near optimal matrix completion"
    - "a simpler approach to matrix completion"
    - "robust principal component analysis"

Connected through hyperedge HE-5 (induced by source paper "efficient transductive online learning via randomized rounding"):
  with members:
    - "near optimal algorithms for online matrix prediction"
    - "relax and localize from value to algorithms"
    - "collaborative filtering in a non uniform world learning with the weighted trace norm"
    - "a stochastic view of optimal regret through minimax duality"
    - "consistency of trace norm minimization"

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)