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: "a new approach to collaborative filtering operator estimation with spectral regularization"
  Abstract: We present a general approach for collaborative filtering (CF) using spectral regularization to learn linear operators from "users" to the "objects" they rate. Recent low-rank type matrix completion approaches to CF are shown to be special cases. However, unlike existing regularization based CF methods, our approach can be used to also incorporate information such as attributes of the users or the objects -- a limitation of existing regularization based CF methods. We then provide novel representer theorems that we use to develop new estimation methods. We provide learning algorithms based on low-rank decompositions, and test them on a standard CF dataset. The experiments indicate the advantages of generalizing the existing regularization based CF methods to incorporate related information about users and objects. Finally, we show that certain multi-task learning methods can be also seen as special cases of our proposed approach.

Connected through hyperedge HE-1 (induced by source paper "a structured prediction approach for conditional meta learning"):
  with members:
    - "meta learning with implicit gradients"
    - "on first order meta learning algorithms"
    - "meta learning with latent embedding optimization"
    - "tadam task dependent adaptive metric for improved few shot learning"
    - "meta learning with differentiable closed form solvers"
    - "prototypical networks for few shot learning"
    - "few shot image recognition by predicting parameters from activations"
    - "optnet differentiable optimization as a layer in neural networks"

Connected through hyperedge HE-2 (induced by source paper "an analysis of noise folding for low rank matrix recovery"):
  with members:
    - "a unified alternating direction method of multipliers by majorization minimization"
    - "stable low rank matrix recovery via null space properties"
    - "noise folding in compressed sensing"
    - "robust principal component analysis"

Connected through hyperedge HE-3 (induced by source paper "branched multi task networks deciding what layers to share"):
  with members:
    - "stochastic filter groups for multi task cnns learning specialist and generalist convolution kernels"
    - "representation similarity analysis for efficient task taxonomy transfer learning"
    - "feature partitioning for efficient multi task architectures"
    - "attentive single tasking of multiple tasks"
    - "a modulation module for multi task learning with applications in image retrieval"
    - "evolutionary architecture search for deep multitask networks"
    - "regularized evolution for image classifier architecture search"
    - "taskonomy disentangling task transfer learning"

Connected through hyperedge HE-4 (induced by source paper "matrix completion via nonconvex regularization convergence of the proximal gradient algorithm"):
  with members:
    - "matrix completion has no spurious local minimum"
    - "guaranteed matrix completion via non convex factorization"
    - "compressed sensing recovery via nonconvex shrinkage penalties"
    - "l_ 1 2 regularization convergence of iterative half thresholding algorithm"
    - "performance guarantees for schatten p quasi norm minimization in recovery of low rank matrices"
    - "restricted strong convexity and weighted matrix completion optimal bounds with noise"
    - "the power of convex relaxation near optimal matrix completion"
    - "a simpler approach to matrix completion"

Connected through hyperedge HE-5 (induced by source paper "leveraging low rank relations between surrogate tasks in structured prediction"):
  with members:
    - "consistent multitask learning with nonlinear output relations"
    - "on structured prediction theory with calibrated convex surrogate losses"
    - "a consistent regularization approach for structured prediction"
    - "consistency of trace norm minimization"

Connected through hyperedge HE-6 (induced by source paper "clustered monotone transforms for rating factorization"):
  with members:
    - "geometric matrix completion with recurrent multi graph neural networks"
    - "neural network matrix factorization"
    - "fast provable algorithms for isotonic regression in all ell_ p norms"
    - "learning to rank with bregman divergences and monotone retargeting"
    - "social knowledge based recommender system application to the movies domain"

Connected through hyperedge HE-7 (induced by source paper "a riemannian gossip approach to subspace learning on grassmann manifold"):
  with members:
    - "riemannian stochastic variance reduced gradient algorithm with retraction and vector transport"
    - "a survey on multi task learning"
    - "dimensionality reduction on spd manifolds the emergence of geometry aware methods"
    - "a riemannian gossip approach to decentralized matrix completion"
    - "manopt a matlab toolbox for optimization on manifolds"
    - "a convex formulation for learning task relationships in multi task learning"
    - "fixed rank matrix factorizations and riemannian low rank optimization"
    - "a convex feature learning formulation for latent task structure discovery"

Connected through hyperedge HE-8 (induced by source paper "collaborative filtering with label consistent restricted boltzmann machine"):
  with members:
    - "hybrid recommender system based on autoencoders"
    - "the power of convex relaxation near optimal matrix completion"
    - "a simpler approach to matrix completion"

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)