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: "better mini batch algorithms via accelerated gradient methods"
  Abstract: Mini-batch algorithms have been proposed as a way to speed-up stochastic convex optimization problems. We study how such algorithms can be improved using accelerated gradient methods. We provide a novel analysis, which shows how standard gradient methods may sometimes be insufficient to obtain a significant speed-up and propose a novel accelerated gradient algorithm, which deals with this deficiency, enjoys a uniformly superior guarantee and works well in practice.

Connected through hyperedge HE-1 (induced by source paper "distributed stochastic algorithms for high rate streaming principal component analysis"):
  with members:
    - "bandit principal component analysis"
    - "exponentially convergent stochastic k pca without variance reduction"
    - "an implicit form of krasulina s k pca update without the orthonormality constraint"
    - "on the regret minimization of nonconvex online gradient ascent for online pca"
    - "on the computational inefficiency of large batch sizes for stochastic gradient descent"
    - "how to make the gradients small stochastically even faster convex and nonconvex sgd"
    - "communication efficient algorithms for distributed stochastic principal component analysis"
    - "follow the compressed leader faster online learning of eigenvectors and faster mmwu"

Connected through hyperedge HE-2 (induced by source paper "adaptive distributed stochastic gradient descent for minimizing delay in the presence of stragglers"):
  with members:
    - "coded cooperative computation for internet of things"
    - "speeding up distributed machine learning using codes"
    - "anytime stochastic gradient descent a time to hear from all the workers"
    - "on the optimal recovery threshold of coded matrix multiplication"
    - "lagrange coded computing optimal design for resiliency security and privacy"
    - "exploitation of stragglers in coded computation"
    - "don t decay the learning rate increase the batch size"
    - "short dot computing large linear transforms distributedly using coded short dot products"

Connected through hyperedge HE-3 (induced by source paper "is local sgd better than minibatch sgd"):
  with members:
    - "the error feedback framework better rates for sgd with delayed gradients and compressed communication"
    - "unified optimal analysis of the stochastic gradient method"
    - "local sgd with periodic averaging tighter analysis and adaptive synchronization"
    - "on the randomized complexity of minimizing a convex quadratic function"
    - "don t use large mini batches use local sgd"
    - "on the convergence properties of a k step averaging stochastic gradient descent algorithm for nonconvex optimization"
    - "cooperative sgd a unified framework for the design and analysis of communication efficient sgd algorithms"
    - "memory and communication efficient distributed stochastic optimization with minibatch prox"

Connected through hyperedge HE-4 (induced by source paper "stochastic gradient coding for straggler mitigation in distributed learning"):
  with members:
    - "fundamental limits of approximate gradient coding"
    - "analyzing the download time of availability codes"
    - "distributed stochastic gradient descent using ldgm codes"
    - "erasurehead distributed gradient descent without delays using approximate gradient coding"
    - "coded cooperative computation for internet of things"
    - "speeding up distributed machine learning using codes"
    - "anytime stochastic gradient descent a time to hear from all the workers"
    - "speeding up distributed gradient descent by utilizing non persistent stragglers"

Connected through hyperedge HE-5 (induced by source paper "the communication complexity of optimization"):
  with members:
    - "distributed and streaming linear programming in low dimensions"
    - "solving linear programs in the current matrix multiplication time"
    - "leveraging well conditioned bases streaming distributed summaries in minkowski p norms"
    - "distributed statistical estimation of matrix products with applications"
    - "l1 regression using lewis weights preconditioning and stochastic gradient descent"
    - "on communication complexity of classification problems"
    - "communication optimal distributed clustering"
    - "distributed low rank approximation of implicit functions of a matrix"

Connected through hyperedge HE-6 (induced by source paper "trajectory normalized gradients for distributed optimization"):
  with members:
    - "sparsified sgd with memory"
    - "signsgd compressed optimisation for non convex problems"
    - "an alternative view when does sgd escape local minima"
    - "cooperative sgd a unified framework for the design and analysis of communication efficient sgd algorithms"
    - "error compensated quantized sgd and its applications to large scale distributed optimization"
    - "self normalizing neural networks"
    - "gradient sparsification for communication efficient distributed optimization"
    - "sparse communication for distributed gradient descent"

Connected through hyperedge HE-7 (induced by source paper "gwu nlp lab at semeval 2019 task 3 emocontext effective contextual information in models for emotion detection in sentence level in a multigenre corpus"):
  with members:
    - "deep contextualized word representations"
    - "ntua slp at semeval 2018 task 1 predicting affective content in tweets with deep attentive rnns and transfer learning"
    - "wassa 2017 shared task on emotion intensity"
    - "advances in pre training distributed word representations"
    - "seernet at emoint 2017 tweet emotion intensity estimator"
    - "a sentiment and semantics based approach for emotion detection in textual conversations"
    - "gated feedback recurrent neural networks"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-8 (induced by source paper "optimal mini batch size selection for fast gradient descent"):
  with members:
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "on large batch training for deep learning generalization gap and sharp minima"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"
    - "on the properties of neural machine translation encoder decoder approaches"
    - "one weird trick for parallelizing convolutional neural networks"
    - "deep learning with elastic averaging sgd"

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)