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 spark based deep learning supporting asynchronous updates and caffe compatibility"
  Abstract: The increasing complexity of deep neural networks (DNNs) has made it challenging to exploit existing large-scale data process 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 simultaneously exploits Apache Spark for large-scale distributed data management and Caffe for GPU-based acceleration. DeepSpark directly accepts Caffe input specifications, providing seamless compatibility with existing designs and network structures. To support parallel operations, DeepSpark automatically distributes workloads and parameters to Caffe-running nodes using Spark and iteratively aggregates training results by a novel lock-free asynchronous variant of the popular elastic averaging stochastic gradient descent (SGD) 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 "homomorphic parameter compression for distributed deep learning training"):
  with members:
    - "end to end interpretation of the french street name signs dataset"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "qsgd communication efficient sgd via gradient quantization and encoding"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "binaryconnect training deep neural networks with binary weights during propagations"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "compressing neural networks with the hashing trick"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-3 (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 a spark based distributed deep learning framework for commodity clusters"
    - "improving computer aided detection using convolutional neural networks and random view aggregation"

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)