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: "implementation of a practical distributed calculation system with browsers and javascript and application to distributed deep learning"
  Abstract: Deep learning can achieve outstanding results in various fields. However, it requires so significant computational power that graphics processing units (GPUs) and/or numerous computers are often required for the practical application. We have developed a new distributed calculation framework called "Sashimi" that allows any computer to be used as a distribution node only by accessing a website. We have also developed a new JavaScript neural network framework called "Sukiyaki" that uses general purpose GPUs with web browsers. Sukiyaki performs 30 times faster than a conventional JavaScript library for deep convolutional neural networks (deep CNNs) learning. The combination of Sashimi and Sukiyaki, as well as new distribution algorithms, demonstrates the distributed deep learning of deep CNNs only with web browsers on various devices. The libraries that comprise the proposed methods are available under MIT license at this http URL

Connected through hyperedge HE-1 (induced by source paper "zeno byzantine suspicious stochastic gradient descent"):
  with members:
    - "byzantine stochastic gradient descent"
    - "generalized byzantine tolerant sgd"
    - "byzantine robust distributed learning towards optimal statistical rates"
    - "distributed statistical machine learning in adversarial settings byzantine gradient descent"
    - "defending non bayesian learning against adversarial attacks"
    - "federated optimization distributed machine learning for on device intelligence"
    - "mlitb machine learning in the browser"

Connected through hyperedge HE-2 (induced by source paper "zeno distributed stochastic gradient descent with suspicion based fault tolerance"):
  with members:
    - "fall of empires breaking byzantine tolerant sgd by inner product manipulation"
    - "byzantine stochastic gradient descent"
    - "byzantine robust distributed learning towards optimal statistical rates"
    - "phocas dimensional byzantine resilient stochastic gradient descent"
    - "distributed statistical machine learning in adversarial settings byzantine gradient descent"
    - "defending non bayesian learning against adversarial attacks"
    - "mlitb machine learning in the browser"

Connected through hyperedge HE-3 (induced by source paper "development of javascript based deep learning platform and application to distributed training"):
  with members:
    - "convolutional neural networks for medical image analysis full training or fine tuning"
    - "deep residual learning for image recognition"
    - "8 bit approximations for parallelism in deep learning"
    - "maxdnn an efficient convolution kernel for deep learning with maxwell gpus"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "mlitb machine learning in the browser"
    - "imagenet large scale visual recognition challenge"

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.DC(Distributed, Parallel, and Cluster Computing)