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: "towards optimal one pass large scale learning with averaged stochastic gradient descent"
  Abstract: For large scale learning problems, it is desirable if we can obtain the optimal model parameters by going through the data in only one pass. Polyak and Juditsky (1992) showed that asymptotically the test performance of the simple average of the parameters obtained by stochastic gradient descent (SGD) is as good as that of the parameters which minimize the empirical cost. However, to our knowledge, despite its optimal asymptotic convergence rate, averaged SGD (ASGD) received little attention in recent research on large scale learning. One possible reason is that it may take a prohibitively large number of training samples for ASGD to reach its asymptotic region for most real problems. In this paper, we present a finite sample analysis for the method of Polyak and Juditsky (1992). Our analysis shows that it indeed usually takes a huge number of samples for ASGD to reach its asymptotic region for improperly chosen learning rate. More importantly, based on our analysis, we propose a simple way to properly set learning rate so that it takes a reasonable amount of data for ASGD to reach its asymptotic region. We compare ASGD using our proposed learning rate with other well known algorithms for training large scale linear classifiers. The experiments clearly show the superiority of ASGD.

Connected through hyperedge HE-1 (induced by source paper "deep learning theory review an optimal control and dynamical systems perspective"):
  with members:
    - "a tail index analysis of stochastic gradient noise in deep neural networks"
    - "a mean field theory of batch normalization"
    - "where is the information in a deep neural network"
    - "dynamical isometry and a mean field theory of lstms and grus"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "gradient descent finds global minima of deep neural networks"
    - "learning overparameterized neural networks via stochastic gradient descent on structured data"

Connected through hyperedge HE-2 (induced by source paper "optimizing multi gpu parallelization strategies for deep learning training"):
  with members:
    - "crossbow scaling deep learning with small batch sizes on multi gpu servers"
    - "findings of the 2016 wmt shared task on cross lingual pronoun prediction"
    - "yet another accelerated sgd resnet 50 training on imagenet in 74 7 seconds"
    - "communication scheduling as a first class citizen in distributed machine learning systems"
    - "scaling neural machine translation"
    - "gpipe efficient training of giant neural networks using pipeline parallelism"
    - "three factors influencing minima in sgd"
    - "on scale out deep learning training for cloud and hpc"

Connected through hyperedge HE-3 (induced by source paper "crossbow scaling deep learning with small batch sizes on multi gpu servers"):
  with members:
    - "superneurons dynamic gpu memory management for training deep neural networks"
    - "three factors influencing minima in sgd"
    - "revisiting small batch training for deep neural networks"
    - "highly scalable deep learning training system with mixed precision training imagenet in four minutes"
    - "horovod fast and easy distributed deep learning in tensorflow"
    - "measuring the effects of data parallelism on neural network training"
    - "in datacenter performance analysis of a tensor processing unit"
    - "ray a distributed framework for emerging ai applications"

Connected through hyperedge HE-4 (induced by source paper "lsh sampling breaks the computation chicken and egg loop in adaptive stochastic gradient estimation"):
  with members:
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "faster coordinate descent via adaptive importance sampling"
    - "stochastic gradient descent for non smooth optimization convergence results and optimal averaging schemes"
    - "fast approximation of matrix coherence and statistical leverage"

Connected through hyperedge HE-5 (induced by source paper "insensitive stochastic gradient twin support vector machines for large scale problems"):
  with members:
    - "accurate large minibatch sgd training imagenet in 1 hour"

Connected through hyperedge HE-6 (induced by source paper "analysis and optimization of convolutional neural network architectures"):
  with members:
    - "on orthogonality and learning recurrent networks with long term dependencies"
    - "systematic evaluation of convolution neural network advances on the imagenet"
    - "convolutional neural fabrics"
    - "exploiting cyclic symmetry in convolutional neural networks"
    - "sgdr stochastic gradient descent with restarts"
    - "aggregated residual transformations for deep neural networks"
    - "deep networks with stochastic depth"
    - "understanding deep learning requires rethinking generalization"

Connected through hyperedge HE-7 (induced by source paper "deep lstm for large vocabulary continuous speech recognition"):
  with members:
    - "revisiting distributed synchronous sgd"
    - "exponential moving average model in parallel speech recognition training"
    - "fast and accurate recurrent neural network acoustic models for speech recognition"
    - "deep residual learning for image recognition"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

Connected through hyperedge HE-8 (induced by source paper "exponential moving average model in parallel speech recognition training"):
  with members:
    - "revisiting distributed synchronous sgd"
    - "fast and accurate recurrent neural network acoustic models for speech recognition"
    - "deep speech 2 end to end speech recognition in english and mandarin"
    - "deep residual learning for image recognition"
    - "neural machine translation by jointly learning to align and translate"

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)