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: "tune a research platform for distributed model selection and training"
  Abstract: Modern machine learning algorithms are increasingly computationally demanding, requiring specialized hardware and distributed computation to achieve high performance in a reasonable time frame. Many hyperparameter search algorithms have been proposed for improving the efficiency of model selection, however their adaptation to the distributed compute environment is often ad-hoc. We propose Tune, a unified framework for model selection and training that provides a narrow-waist interface between training scripts and search algorithms. We show that this interface meets the requirements for a broad range of hyperparameter search algorithms, allows straightforward scaling of search to large clusters, and simplifies algorithm implementation. We demonstrate the implementation of several state-of-the-art hyperparameter search algorithms in Tune. Tune is available at this http URL

Connected through hyperedge HE-1 (induced by source paper "hyper parameter optimization a review of algorithms and applications"):
  with members:
    - "a generalized framework for population based training"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "meta learning a survey"
    - "deep learning using rectified linear units relu"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "massively parallel hyperparameter tuning"
    - "combination of hyperband and bayesian optimization for hyperparameter optimization in deep learning"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"

Connected through hyperedge HE-2 (induced by source paper "understanding and optimizing packed neural network training for hyper parameter tuning"):
  with members:
    - "hypersched dynamic resource reallocation for model development on a deadline"
    - "crossbow scaling deep learning with small batch sizes on multi gpu servers"
    - "3lc lightweight and effective traffic compression for distributed machine learning"
    - "efficient memory management for gpu based deep learning systems"
    - "themis fair and efficient gpu cluster scheduling for machine learning workloads"
    - "exploiting reuse in pipeline aware hyperparameter tuning"
    - "analysis of large scale multi tenant gpu clusters for dnn training workloads"
    - "energy and policy considerations for deep learning in nlp"

Connected through hyperedge HE-3 (induced by source paper "one shot bayes opt with probabilistic population based training"):
  with members:
    - "a generalized framework for population based training"
    - "emergent coordination through competition"
    - "towards explaining the regularization effect of initial large learning rate in training neural networks"
    - "fast efficient hyperparameter tuning for policy gradients"
    - "bayesian optimization under heavy tailed payoffs"
    - "massively parallel hyperparameter tuning"
    - "bayesian optimization in alphago"
    - "deep reinforcement learning that matters"

Connected through hyperedge HE-4 (induced by source paper "hypersched dynamic resource reallocation for model development on a deadline"):
  with members:
    - "massively parallel hyperparameter tuning"
    - "an argument in favor of strong scaling for deep neural networks with small datasets"
    - "tiresias predicting security events through deep learning"
    - "large batch size training of neural networks with adversarial training and second order information"
    - "highly scalable deep learning training system with mixed precision training imagenet in four minutes"
    - "slaq quality driven scheduling for distributed machine learning"
    - "deep reinforcement learning that matters"
    - "rllib abstractions for distributed reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "a system for massively parallel hyperparameter tuning"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "a generalized framework for population based training"
    - "darts differentiable architecture search"
    - "scaling sgd batch size to 32k for imagenet training"
    - "regularizing and optimizing lstm language models"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "hyperband a novel bandit based approach to hyperparameter optimization"
    - "fast bayesian optimization of machine learning hyperparameters on large datasets"

Connected through hyperedge HE-6 (induced by source paper "hoplite efficient collective communication for task based distributed systems"):
  with members:
    - "towards scalable dataframe systems"
    - "blink fast and generic collectives for distributed ml"
    - "pytorch an imperative style high performance deep learning library"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "horovod fast and easy distributed deep learning in tensorflow"
    - "ray a distributed framework for emerging ai applications"
    - "rllib abstractions for distributed reinforcement learning"
    - "proximal policy optimization algorithms"

Connected through hyperedge HE-7 (induced by source paper "a framework for democratizing ai"):
  with members:
    - "explainable ai for trees from local explanations to global understanding"
    - "a generic framework for privacy preserving deep learning"
    - "deep neural decision trees"
    - "auto keras an efficient neural architecture search system"
    - "ray a distributed framework for emerging ai applications"
    - "xgboost a scalable tree boosting system"
    - "why should i trust you explaining the predictions of any classifier"
    - "mxnet a flexible and efficient machine learning library for heterogeneous distributed systems"

Connected through hyperedge HE-8 (induced by source paper "gesticulator a framework for semantically aware speech driven gesture generation"):
  with members:
    - "moglow probabilistic and controllable motion synthesis using normalising flows"
    - "analyzing input and output representations for speech driven gesture generation"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "speech driven animation with meaningful behaviors"
    - "film visual reasoning with a general conditioning layer"
    - "adam a method for stochastic optimization"

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)