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: "mlitb machine learning in the browser"
  Abstract: With few exceptions, the field of Machine Learning (ML) research has largely ignored the browser as a computational engine. Beyond an educational resource for ML, the browser has vast potential to not only improve the state-of-the-art in ML research, but also, inexpensively and on a massive scale, to bring sophisticated ML learning and prediction to the public at large. This paper introduces MLitB, a prototype ML framework written entirely in JavaScript, capable of performing large-scale distributed computing with heterogeneous classes of devices. The development of MLitB has been driven by several underlying objectives whose aim is to make ML learning and usage ubiquitous (by using ubiquitous compute devices), cheap and effortlessly distributed, and collaborative. This is achieved by allowing every internet capable device to run training algorithms and predictive models with no software installation and by saving models in universally readable formats. Our prototype library is capable of training deep neural networks with synchronized, distributed stochastic gradient descent. MLitB offers several important opportunities for novel ML research, including: development of distributed learning algorithms, advancement of web GPU algorithms, novel field and mobile applications, privacy preserving computing, and green grid-computing. MLitB is available as open source software.

Connected through hyperedge HE-1 (induced by source paper "jsdoop and tensorflow js volunteer distributed web browser based neural network training"):
  with members:
    - "tensorflow js machine learning for the web and beyond"
    - "towards federated learning at scale system design"
    - "moving deep learning into web browser how far can we go"
    - "boinc a platform for volunteer computing"
    - "deep learning towards mobile applications"
    - "deep learning in mobile and wireless networking a survey"
    - "deep gradient compression reducing the communication bandwidth for distributed training"
    - "sparse communication for distributed gradient descent"

Connected through hyperedge HE-2 (induced by source paper "moving deep learning into web browser how far can we go"):
  with members:
    - "inverse reinforcement learning for video games"
    - "ai benchmark running deep neural networks on android smartphones"
    - "not just privacy improving performance of private deep learning in mobile cloud"
    - "the need for speed of ai applications performance comparison of native vs browser based algorithm implementations"
    - "performance modeling and evaluation of distributed deep learning frameworks on gpus"
    - "distributed deep neural networks over the cloud the edge and end devices"
    - "deepsense a unified deep learning framework for time series mobile sensing data processing"
    - "tensorflow a system for large scale machine learning"

Connected through hyperedge HE-3 (induced by source paper "genet a quickly scalable fat tree overlay for personal volunteer computing using webrtc"):
  with members:
    - "personal volunteer computing"
    - "pando personal volunteer computing in browsers"
    - "pando a volunteer computing platform for the web"

Connected through hyperedge HE-4 (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"
    - "implementation of a practical distributed calculation system with browsers and javascript and application to distributed deep learning"

Connected through hyperedge HE-5 (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"
    - "implementation of a practical distributed calculation system with browsers and javascript and application to distributed deep learning"

Connected through hyperedge HE-6 (induced by source paper "pando a volunteer computing platform for the web"):
  with members:
    - "not so fast analyzing the performance of webassembly vs native code"
    - "boinc a platform for volunteer computing"
    - "genet a quickly scalable fat tree overlay for personal volunteer computing using webrtc"
    - "personal volunteer computing"
    - "cloud computing and grid computing 360 degree compared"
    - "the anatomy of the grid enabling scalable virtual organizations"

Connected through hyperedge HE-7 (induced by source paper "pando personal volunteer computing in browsers"):
  with members:
    - "not so fast analyzing the performance of webassembly vs native code"
    - "boinc a platform for volunteer computing"
    - "genet a quickly scalable fat tree overlay for personal volunteer computing using webrtc"
    - "personal volunteer computing"

Connected through hyperedge HE-8 (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"
    - "implementation of a practical distributed calculation system with browsers and javascript and application to distributed deep learning"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "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)