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: "deepspark a spark based distributed deep learning framework for commodity clusters"
  Abstract: The increasing complexity of deep neural networks (DNNs) has made it challenging to exploit existing large-scale data processing pipelines for handling massive data and parameters involved in DNN training. Distributed computing platforms and GPGPU-based acceleration provide a mainstream solution to this computational challenge. In this paper, we propose DeepSpark, a distributed and parallel deep learning framework that exploits Apache Spark on commodity clusters. To support parallel operations, DeepSpark automatically distributes workloads and parameters to Caffe/Tensorflow-running nodes using Spark, and iteratively aggregates training results by a novel lock-free asynchronous variant of the popular elastic averaging stochastic gradient descent based update scheme, effectively complementing the synchronized processing capabilities of Spark. DeepSpark is an on-going project, and the current release is available at this http URL

Connected through hyperedge HE-1 (induced by source paper "deep leakage from gradients"):
  with members:
    - "towards federated learning at scale system design"
    - "exploiting unintended feature leakage in collaborative learning"
    - "federated learning strategies for improving communication efficiency"
    - "variance based gradient compression for efficient distributed deep learning"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "highly scalable deep learning training system with mixed precision training imagenet in four minutes"
    - "horovod fast and easy distributed deep learning in tensorflow"
    - "deep gradient compression reducing the communication bandwidth for distributed training"

Connected through hyperedge HE-2 (induced by source paper "pipe sgd a decentralized pipelined sgd framework for distributed deep net training"):
  with members:
    - "pipedream fast and efficient pipeline parallel dnn training"
    - "deep gradient compression reducing the communication bandwidth for distributed training"
    - "adacomp adaptive residual gradient compression for data parallel distributed training"
    - "terngrad ternary gradients to reduce communication in distributed deep learning"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "qsgd communication efficient sgd via gradient quantization and encoding"
    - "fractalnet ultra deep neural networks without residuals"
    - "tensorflow a system for large scale machine learning"

Connected through hyperedge HE-3 (induced by source paper "a survey on spark ecosystem for big data processing"):
  with members:
    - "sparkle optimizing spark for large memory machines and analytics"
    - "keystoneml optimizing pipelines for large scale advanced analytics"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "mllib machine learning in apache spark"
    - "splash user friendly programming interface for parallelizing stochastic algorithms"
    - "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-4 (induced by source paper "mxnet mpi embedding mpi parallelism in parameter server task model for scaling deep learning"):
  with members:
    - "revisiting distributed synchronous sgd"
    - "virtualizing deep neural networks for memory efficient neural network design"
    - "an overview of gradient descent optimization algorithms"
    - "tensorflow a system for large scale machine learning"
    - "mxnet a flexible and efficient machine learning library for heterogeneous distributed systems"
    - "deep learning with elastic averaging sgd"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-5 (induced by source paper "efficient embedding of mpi collectives in mxnet dags for scaling deep learning"):
  with members:
    - "deep residual learning for image recognition"

Connected through hyperedge HE-6 (induced by source paper "deep learning in bioinformatics"):
  with members:
    - "brain tumor segmentation with deep neural networks"
    - "deep belief networks used on high resolution multichannel electroencephalography data for seizure detection"
    - "deep motif visualizing genomic sequence classifications"
    - "deepmirgene deep neural network based precursor microrna prediction"
    - "comparative study of deep learning software frameworks"
    - "deeptarget end to end learning framework for microrna target prediction using deep recurrent neural networks"
    - "deepspark spark based deep learning supporting asynchronous updates and caffe compatibility"
    - "improving computer aided detection using convolutional neural networks and random view aggregation"

Connected through hyperedge HE-7 (induced by source paper "distributed stochastic optimization for deep learning thesis"):
  with members:
    - "distributed deep learning using synchronous stochastic gradient descent"
    - "distributed deep learning for answer selection"
    - "convolutional neural networks with low rank regularization"
    - "dynamics of stochastic gradient algorithms"
    - "train faster generalize better stability of stochastic gradient descent"
    - "staleness aware async sgd for distributed deep learning"
    - "the loss surfaces of multilayer networks"
    - "overfeat integrated recognition localization and detection using convolutional networks"

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)