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: "on the functional equivalence of tsk fuzzy systems to neural networks mixture of experts cart and stacking ensemble regression"
  Abstract: Fuzzy systems have achieved great success in numerous applications. However, there are still many challenges in designing an optimal fuzzy system, e.g., how to efficiently train its parameters, how to improve its performance without adding too many parameters, how to balance the trade-off between cooperations and competitions among the rules, how to overcome the curse of dimensionality, etc. Literature has shown that by making appropriate connections between fuzzy systems and other machine learning approaches, good practices from other domains may be used to improve the fuzzy systems, and vice versa. This paper gives an overview on the functional equivalence between Takagi-Sugeno-Kang fuzzy systems and four classic machine learning approaches -- neural networks, mixture of experts, classification and regression trees, and stacking ensemble regression -- for regression problems. We also point out some promising new research directions, inspired by the functional equivalence, that could lead to solutions to the aforementioned problems. To our knowledge, this is so far the most comprehensive overview on the connections between fuzzy systems and other popular machine learning approaches, and hopefully will stimulate more hybridization between different machine learning algorithms.

Connected through hyperedge HE-1 (induced by source paper "optimize tsk fuzzy systems for classification problems mini batch gradient descent with uniform regularization and batch normalization"):
  with members:
    - "mixture models for diverse machine translation tricks of the trade"
    - "adaptive gradient methods with dynamic bound of learning rate"
    - "group normalization"
    - "revisiting small batch training for deep neural networks"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "revisit fuzzy neural network demystifying batch normalization and relu with generalized hamming network"
    - "improving generalization performance by switching from adam to sgd"
    - "a survey on soft subspace clustering"

Connected through hyperedge HE-2 (induced by source paper "mbgd rda training and rule pruning for concise tsk fuzzy regression models"):
  with members:
    - "stabilizing the lottery ticket hypothesis"
    - "adaptive gradient methods with dynamic bound of learning rate"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "mbgd rda training and similarity based rule pruning for concise tsk fuzzy regression models"):
  with members:
    - "adaptive gradient methods with dynamic bound of learning rate"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "optimize tsk fuzzy systems for big data classification problems bag of tricks"):
  with members:
    - "mixture models for diverse machine translation tricks of the trade"
    - "adaptive gradient methods with dynamic bound of learning rate"
    - "group normalization"
    - "revisiting small batch training for deep neural networks"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "revisit fuzzy neural network demystifying batch normalization and relu with generalized hamming network"
    - "improving generalization performance by switching from adam to sgd"
    - "densely connected convolutional networks"

Connected through hyperedge HE-5 (induced by source paper "optimize tsk fuzzy systems for big data regression problems mini batch gradient descent with regularization droprule and adabound mbgd rda"):
  with members:
    - "adaptive gradient methods with dynamic bound of learning rate"
    - "adam a method for stochastic optimization"
    - "practical recommendations for gradient based training of deep architectures"

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)