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 method of moments for mixture models and hidden markov models"
  Abstract: Mixture models are a fundamental tool in applied statistics and machine learning for treating data taken from multiple subpopulations. The current practice for estimating the parameters of such models relies on local search heuristics (e.g., the EM algorithm) which are prone to failure, and existing consistent methods are unfavorable due to their high computational and sample complexity which typically scale exponentially with the number of mixture components. This work develops an efficient method of moments approach to parameter estimation for a broad class of high-dimensional mixture models with many components, including multi-view mixtures of Gaussians (such as mixtures of axis-aligned Gaussians) and hidden Markov models. The new method leads to rigorous unsupervised learning results for mixture models that were not achieved by previous works; and, because of its simplicity, it offers a viable alternative to EM for practical deployment.

Connected through hyperedge HE-1 (induced by source paper "provable meta learning of linear representations"):
  with members:
    - "adaptive gradient based meta learning methods"
    - "learning to learn stochastic gradient descent with biased regularization"
    - "online meta learning"
    - "multi task deep neural networks for natural language understanding"
    - "cloze driven pretraining of self attention networks"
    - "provable guarantees for gradient based meta learning"
    - "characterizing and avoiding negative transfer"
    - "how to escape saddle points efficiently"

Connected through hyperedge HE-2 (induced by source paper "regime switching bandits"):
  with members:
    - "a survey on practical applications of multi armed and contextual bandits"
    - "restless hidden markov bandits with linear rewards"
    - "introduction to multi armed bandits"
    - "learning to optimize under non stationarity"
    - "multi armed bandits for correlated markovian environments with smoothed reward feedback"
    - "exploration bonus for regret minimization in undiscounted discrete and continuous markov decision processes"
    - "reinforcement learning of pomdp s using spectral methods"
    - "reinforcement learning of pomdps using spectral methods"

Connected through hyperedge HE-3 (induced by source paper "regularized learning for domain adaptation under label shifts"):
  with members:
    - "detecting and correcting for label shift with black box predictors"
    - "active learning for cost sensitive classification"
    - "reinforcement learning of pomdp s using spectral methods"
    - "reinforcement learning of pomdps using spectral methods"
    - "mixture proportion estimation via kernel embedding of distributions"
    - "deep residual learning for image recognition"
    - "statistical linear estimation with penalized estimators an application to reinforcement learning"
    - "on causal and anticausal learning"

Connected through hyperedge HE-4 (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-5 (induced by source paper "smoothed analysis in unsupervised learning via decoupling"):
  with members:
    - "smoothed analysis of discrete tensor decomposition and assemblies of neurons"
    - "learning overcomplete hmms"
    - "local max cut in smoothed polynomial time"
    - "polynomial time tensor decompositions with sum of squares"
    - "learning mixtures of gaussians in high dimensions"
    - "fast spectral algorithms from sum of squares proofs tensor decomposition and planted sparse vectors"
    - "minimal realization problems for hidden markov models"
    - "dictionary learning and tensor decomposition via the sum of squares method"

Connected through hyperedge HE-6 (induced by source paper "moment based quantile sketches for efficient high cardinality aggregation queries"):
  with members:
    - "an efficient bandit algorithm for realtime multivariate optimization"
    - "macrobase prioritizing attention in fast data"
    - "spectrum estimation from samples"
    - "an experimental study of distributed quantile estimation"
    - "blinkdb queries with bounded errors and bounded response times on very large data"
    - "polynomial learning of distribution families"
    - "medians and beyond new aggregation techniques for sensor networks"

Connected through hyperedge HE-7 (induced by source paper "learning hidden markov models from pairwise co occurrences with applications to topic modeling"):
  with members:
    - "learning overcomplete hmms"
    - "a practical algorithm for topic modeling with provable guarantees"
    - "tensor decompositions for learning latent variable models"
    - "a spectral algorithm for learning hidden markov models"

Connected through hyperedge HE-8 (induced by source paper "long term forecasting using higher order tensor rnns"):
  with members:
    - "learning to reason with third order tensor products"
    - "deepar probabilistic forecasting with autoregressive recurrent networks"
    - "expressive power of recurrent neural networks"
    - "tensor train recurrent neural networks for video classification"
    - "generating long term trajectories using deep hierarchical networks"
    - "learning from multiway data simple and efficient tensor regression"
    - "higher order recurrent neural networks"
    - "hierarchical multiscale recurrent neural networks"

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)