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 spectral algorithm for learning hidden markov models"
  Abstract: Hidden Markov Models (HMMs) are one of the most fundamental and widely used statistical tools for modeling discrete time series. In general, learning HMMs from data is computationally hard (under cryptographic assumptions), and practitioners typically resort to search heuristics which suffer from the usual local optima issues. We prove that under a natural separation condition (bounds on the smallest singular value of the HMM parameters), there is an efficient and provably correct algorithm for learning HMMs. The sample complexity of the algorithm does not explicitly depend on the number of distinct (discrete) observations---it implicitly depends on this quantity through spectral properties of the underlying HMM. This makes the algorithm particularly applicable to settings with a large number of observations, such as those in natural language processing where the space of observation is sometimes the words in a language. The algorithm is also simple, employing only a singular value decomposition and matrix multiplications.

Connected through hyperedge HE-1 (induced by source paper "combining offline models and online monte carlo tree search for planning from scratch"):
  with members:
    - "bayesian reinforcement learning in factored pomdps"
    - "learning in pomdps with monte carlo tree search"
    - "despot online pomdp planning with regularization"
    - "bayesian reinforcement learning a survey"
    - "efficient learning and planning with compressed predictive states"
    - "predictive state representations a new theory for modeling dynamical systems"
    - "heuristic search value iteration for pomdps"
    - "learning to make predictions in partially observable environments without a generative model"

Connected through hyperedge HE-2 (induced by source paper "learning deterministic weighted automata with queries and counterexamples"):
  with members:
    - "weighted automata extraction from recurrent neural networks via regression on state spaces"
    - "explaining black boxes on sequential data using weighted automata"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "connecting weighted automata and recurrent neural networks through spectral learning"
    - "attention is all you need"
    - "a maximum matching algorithm for basis selection in spectral learning"
    - "extracting automata from recurrent neural networks using queries and counterexamples"
    - "prediction with a short memory"

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"
    - "a method of moments for mixture models and hidden markov models"

Connected through hyperedge HE-4 (induced by source paper "online learning and planning in partially observable domains without prior knowledge"):
  with members:
    - "combining offline models and online monte carlo tree search for planning from scratch"
    - "bayesian reinforcement learning in factored pomdps"
    - "learning in pomdps with monte carlo tree search"
    - "bayesian reinforcement learning a survey"
    - "efficient learning and planning with compressed predictive states"
    - "predictive state representations a new theory for modeling dynamical systems"
    - "heuristic search value iteration for pomdps"
    - "online planning algorithms for pomdps"

Connected through hyperedge HE-5 (induced by source paper "dynamic local regret for non convex online forecasting"):
  with members:
    - "spectral filtering for general linear dynamical systems"
    - "deepar probabilistic forecasting with autoregressive recurrent networks"
    - "efficient regret minimization in non convex games"
    - "online forecasting matrix factorization"
    - "dynamic regret of strongly adaptive methods"
    - "strongly adaptive online learning"
    - "online learning for time series prediction"

Connected through hyperedge HE-6 (induced by source paper "singular value automata and approximate minimization"):
  with members:
    - "a canonical form for weighted automata and applications to approximate minimization"
    - "stability and complexity of minimising probabilistic automata"
    - "reduced rank hidden markov models"
    - "closing the learning planning loop with predictive state representations"

Connected through hyperedge HE-7 (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-8 (induced by source paper "connecting weighted automata and recurrent neural networks through spectral learning"):
  with members:
    - "rational recurrences"
    - "explaining black boxes on sequential data using weighted automata"
    - "expressive power of recurrent neural networks"
    - "tensor train recurrent neural networks for video classification"
    - "extracting automata from recurrent neural networks using queries and counterexamples"
    - "compressing recurrent neural network with tensor train"
    - "subregular complexity and deep learning"
    - "long term forecasting using higher order tensor rnns"

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)