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: "low rank matrix factorization with attributes"
  Abstract: We develop a new collaborative filtering (CF) method that combines both previously known users' preferences, i.e. standard CF, as well as product/user attributes, i.e. classical function approximation, to predict a given user's interest in a particular product. Our method is a generalized low rank matrix completion problem, where we learn a function whose inputs are pairs of vectors -- the standard low rank matrix completion problem being a special case where the inputs to the function are the row and column indices of the matrix. We solve this generalized matrix completion problem using tensor product kernels for which we also formally generalize standard kernel properties. Benchmark experiments on movie ratings show the advantages of our generalized matrix completion method over the standard matrix completion one with no information about movies or people, as well as over standard multi-task or single task learning methods.

Connected through hyperedge HE-1 (induced by source paper "weighted matrix completion from non random non uniform sampling patterns"):
  with members:
    - "matrix completion with deterministic pattern a geometric perspective"
    - "on deterministic sampling patterns for robust low rank matrix completion"
    - "fundamental conditions for low cp rank tensor completion"
    - "rank determination for low rank data completion"
    - "matrix completion via max norm constrained optimization"
    - "weighted matrix completion and recovery with prior subspace information"
    - "recovery guarantee of weighted low rank approximation via alternating minimization"
    - "mc 2 a two phase algorithm for leveraged matrix completion"

Connected through hyperedge HE-2 (induced by source paper "beyond personalization research directions in multistakeholder recommendation"):
  with members:
    - "using image fairness representations in diversity based re ranking for recommendations"
    - "a fairness aware hybrid recommender system"
    - "personalizing fairness aware re ranking"
    - "synthetic attribute data for evaluating consumer side fairness"
    - "an algorithmic framework to control bias in bandit based personalization"
    - "exploring author gender in book rating and recommendation"
    - "price and profit awareness in recommender systems"
    - "beyond parity fairness objectives for collaborative filtering"

Connected through hyperedge HE-3 (induced by source paper "online matrix completion with side information"):
  with members:
    - "matrix completion on graphs"
    - "online similarity prediction of networked data from known and unknown graphs"
    - "near optimal algorithms for online matrix prediction"
    - "the power of convex relaxation near optimal matrix completion"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-4 (induced by source paper "factorizing lambdamart for cold start recommendations"):
  with members:
    - "matrix completion on graphs"
    - "learning heterogeneous similarity measures for hybrid recommendations in meta mining"

Connected through hyperedge HE-5 (induced by source paper "truncated nuclear norm minimization for image restoration based on iterative support detection"):
  with members:
    - "sparse signal reconstruction via iterative support detection"
    - "the power of convex relaxation near optimal matrix completion"
    - "exact matrix completion via convex optimization"
    - "a new approach to collaborative filtering operator estimation with spectral regularization"

Connected through hyperedge HE-6 (induced by source paper "multi task sparse structure learning"):
  with members:
    - "a convex formulation for learning task relationships in multi task learning"

Connected through hyperedge HE-7 (induced by source paper "analysis of a collaborative filter based on popularity amongst neighbors"):
  with members:
    - "a channel coding perspective of collaborative filtering"
    - "the power of convex relaxation near optimal matrix completion"
    - "matrix completion from a few entries"
    - "a channel coding perspective of recommendation systems"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-8 (induced by source paper "weighted algorithms for compressed sensing and matrix completion"):
  with members:
    - "recovering low rank matrices from few coefficients in any basis"
    - "the power of convex relaxation near optimal matrix completion"
    - "matrix completion from a few entries"
    - "a simpler approach to matrix completion"
    - "sparse recovery by non convex optimization instance optimality"
    - "exact matrix completion via convex optimization"

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)