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: "consistency of trace norm minimization"
  Abstract: Regularization by the sum of singular values, also referred to as the trace norm, is a popular technique for estimating low rank rectangular matrices. In this paper, we extend some of the consistency results of the Lasso to provide necessary and sufficient conditions for rank consistency of trace norm minimization with the square loss. We also provide an adaptive version that is rank consistent even when the necessary condition for the non adaptive version is not fulfilled.

Connected through hyperedge HE-1 (induced by source paper "synthesising 3d facial motion from in the wild speech"):
  with members:
    - "visemenet audio driven animator centric speech animation"
    - "online and linear time attention by enforcing monotonic alignments"
    - "lip reading sentences in the wild"
    - "joint face detection and alignment using multitask cascaded convolutional networks"
    - "effective approaches to attention based neural machine translation"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "adam a method for stochastic optimization"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-2 (induced by source paper "error analysis on graph laplacian regularized estimator"):
  with members:
    - "alternating linear bandits for online matrix factorization recommendation"
    - "graph laplacian regularization for image denoising analysis in the continuous domain"
    - "network lasso clustering and optimization in large graphs"
    - "matrix completion on graphs"
    - "restricted strong convexity and weighted matrix completion optimal bounds with noise"

Connected through hyperedge HE-3 (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"
    - "a new approach to collaborative filtering operator estimation with spectral regularization"

Connected through hyperedge HE-4 (induced by source paper "how much restricted isometry is needed in nonconvex matrix recovery"):
  with members:
    - "global optimality in low rank matrix optimization"
    - "understanding deep learning requires rethinking generalization"
    - "matrix completion has no spurious local minimum"
    - "a geometric analysis of phase retrieval"
    - "train faster generalize better stability of stochastic gradient descent"
    - "escaping from saddle points online stochastic gradient for tensor decomposition"
    - "a reliable effective terascale linear learning system"
    - "the power of convex relaxation near optimal matrix completion"

Connected through hyperedge HE-5 (induced by source paper "a distributed frank wolfe framework for learning low rank matrices with the trace norm"):
  with members:
    - "variance reduced and projection free stochastic optimization"
    - "deep residual learning for image recognition"
    - "imagenet large scale visual recognition challenge"
    - "a distributed frank wolfe algorithm for communication efficient sparse learning"
    - "projection free online learning"
    - "recovering low rank matrices from few coefficients in any basis"
    - "phase retrieval via matrix completion"
    - "the power of convex relaxation near optimal matrix completion"

Connected through hyperedge HE-6 (induced by source paper "regularized loss minimizers with local data perturbation consistency and data irrecoverability"):
  with members:
    - "rappor randomized aggregatable privacy preserving ordinal response"
    - "differentially private empirical risk minimization efficient algorithms and tight error bounds"
    - "learning structural changes of gaussian graphical models in controlled experiments"
    - "calibrating data to sensitivity in private data analysis"
    - "estimation of simultaneously sparse and low rank matrices"
    - "network flow algorithms for structured sparsity"
    - "information theoretic limits of selecting binary graphical models in high dimensions"
    - "differentially private empirical risk minimization"

Connected through hyperedge HE-7 (induced by source paper "efficient tensor completion for color image and video recovery low rank tensor train"):
  with members:
    - "optimal feature extraction and classification of tensors via matrix product state decomposition"
    - "parallel matrix factorization for low rank tensor completion"
    - "bayesian cp factorization of incomplete tensors with automatic rank determination"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-8 (induced by source paper "reexamining low rank matrix factorization for trace norm regularization"):
  with members:
    - "matrix completion has no spurious local minimum"
    - "global optimality in tensor factorization deep learning and beyond"
    - "sketching as a tool for numerical linear algebra"

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)