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 mixtures of linear classifiers"
  Abstract: We consider a discriminative learning (regression) problem, whereby the regression function is a convex combination of k linear classifiers. Existing approaches are based on the EM algorithm, or similar techniques, without provable guarantees. We develop a simple method based on spectral techniques and a `mirroring' trick, that discovers the subspace spanned by the classifiers' parameter vectors. Under a probabilistic assumption on the feature vector distribution, we prove that this approach has nearly optimal statistical efficiency.

Connected through hyperedge HE-1 (induced by source paper "breaking the gridlock in mixture of experts consistent and efficient algorithms"):
  with members:
    - "deep mixture of experts via shallow embedding"
    - "tafe net task aware feature embeddings for efficient learning and inference"
    - "learning one hidden layer neural networks with landscape design"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "beating the perils of non convexity guaranteed training of neural networks using tensor methods"
    - "gradient descent learns one hidden layer cnn don t be afraid of spurious local minima"
    - "attention is all you need"
    - "orthogonalized als a theoretically principled tensor decomposition algorithm for practical use"

Connected through hyperedge HE-2 (induced by source paper "learning mixtures of sparse linear regressions using sparse graph codes"):
  with members:
    - "solving a mixture of many random linear equations by tensor decomposition and alternating minimization"
    - "nonadaptive group testing with random set of defectives"
    - "fast and robust compressive phase retrieval with sparse graph codes"
    - "saffron a fast efficient and robust framework for group testing based on sparse graph codes"
    - "phasecode fast and efficient compressive phase retrieval based on sparse graph codes"
    - "sub linear time support recovery for compressed sensing using sparse graph codes"
    - "guess who rated this movie identifying users through subspace clustering"
    - "spectral experts for estimating mixtures of linear regressions"

Connected through hyperedge HE-3 (induced by source paper "provable tensor methods for learning mixtures of generalized linear models"):
  with members:
    - "sample complexity analysis for learning overcomplete latent variable models through tensor methods"
    - "learning mixtures of ranking models"
    - "guaranteed non orthogonal tensor decomposition via alternating rank 1 updates"
    - "score function features for discriminative learning matrix and tensor framework"
    - "fast detection of overlapping communities via online tensor methods"
    - "discriminative features via generalized eigenvectors"
    - "nonparametric estimation of multi view latent variable models"
    - "online tensor methods for learning latent variable models"

Connected through hyperedge HE-4 (induced by source paper "provable methods for training neural networks with sparse connectivity"):
  with members:
    - "provable bounds for learning some deep representations"
    - "low rank approximations for conditional feedforward computation in deep neural networks"
    - "what regularized auto encoders learn from the data generating distribution"
    - "tensor decompositions for learning latent variable models"
    - "hypercontractivity sum of squares proofs and their applications"
    - "exact recovery of sparsely used dictionaries"

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)