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: "splash user friendly programming interface for parallelizing stochastic algorithms"
  Abstract: Stochastic algorithms are efficient approaches to solving machine learning and optimization problems. In this paper, we propose a general framework called Splash for parallelizing stochastic algorithms on multi-node distributed systems. Splash consists of a programming interface and an execution engine. Using the programming interface, the user develops sequential stochastic algorithms without concerning any detail about distributed computing. The algorithm is then automatically parallelized by a communication-efficient execution engine. We provide theoretical justifications on the optimal rate of convergence for parallelizing stochastic gradient descent. Splash is built on top of Apache Spark. The real-data experiments on logistic regression, collaborative filtering and topic modeling verify that Splash yields order-of-magnitude speedup over single-thread stochastic algorithms and over state-of-the-art implementations on Spark.

Connected through hyperedge HE-1 (induced by source paper "a survey on spark ecosystem for big data processing"):
  with members:
    - "sparkle optimizing spark for large memory machines and analytics"
    - "deepspark a spark based distributed deep learning framework for commodity clusters"
    - "keystoneml optimizing pipelines for large scale advanced analytics"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "mllib machine learning in apache spark"
    - "caffe convolutional architecture for fast feature embedding"
    - "the missing piece in complex analytics low latency scalable model management and serving with velox"
    - "shark sql and rich analytics at scale"

Connected through hyperedge HE-2 (induced by source paper "hemingway modeling distributed optimization algorithms"):
  with members:
    - "rl 2 fast reinforcement learning via slow reinforcement learning"
    - "learning to learn by gradient descent by gradient descent"
    - "omnivore an optimizer for multi device deep learning on cpus and gpus"
    - "learning to compose neural networks for question answering"
    - "keystoneml optimizing pipelines for large scale advanced analytics"
    - "designing neural network architectures using reinforcement learning"
    - "adding vs averaging in distributed primal dual optimization"
    - "mllib machine learning in apache spark"

Connected through hyperedge HE-3 (induced by source paper "revisiting distributed synchronous sgd"):
  with members:
    - "distributed deep learning using synchronous stochastic gradient descent"
    - "conditional image generation with pixelcnn decoders"
    - "exploring the limits of language modeling"
    - "on large batch training for deep learning generalization gap and sharp minima"
    - "on variance reduction in stochastic gradient descent and its asynchronous variants"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "draw a recurrent neural network for image generation"
    - "staleness aware async sgd for distributed deep learning"

Connected through hyperedge HE-4 (induced by source paper "parallelizing word2vec in shared and distributed memory"):
  with members:
    - "adabatch adaptive batch sizes for training deep neural networks"
    - "parallelizing word2vec in multi core and many core architectures"
    - "on large batch training for deep learning generalization gap and sharp minima"
    - "effective approaches to attention based neural machine translation"
    - "ask me anything dynamic memory networks for natural language processing"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "sequence to sequence learning with neural networks"
    - "one billion word benchmark for measuring progress in statistical language modeling"

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)