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: "slaq quality driven scheduling for distributed machine learning"
  Abstract: Training machine learning (ML) models with large datasets can incur significant resource contention on shared clusters. This training typically involves many iterations that continually improve the quality of the model. Yet in exploratory settings, better models can be obtained faster by directing resources to jobs with the most potential for improvement. We describe SLAQ, a cluster scheduling system for approximate ML training jobs that aims to maximize the overall job quality. When allocating cluster resources, SLAQ explores the quality-runtime trade-offs across multiple jobs to maximize system-wide quality improvement. To do so, SLAQ leverages the iterative nature of ML training algorithms, by collecting quality and resource usage information from concurrent jobs, and then generating highly-tailored quality-improvement predictions for future iterations. Experiments show that SLAQ achieves an average quality improvement of up to 73% and an average delay reduction of up to 44% on a large set of ML training jobs, compared to resource fairness schedulers.

Connected through hyperedge HE-1 (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"
    - "tune a research platform for distributed model selection and training"
    - "highly scalable deep learning training system with mixed precision training imagenet in four minutes"
    - "deep reinforcement learning that matters"
    - "rllib abstractions for distributed reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "dl2 a deep learning driven scheduler for deep learning clusters"):
  with members:
    - "experience driven networking a deep reinforcement learning based approach"
    - "online job scheduling in distributed machine learning clusters"
    - "learning scheduling algorithms for data processing clusters"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "starcraft ii a new challenge for reinforcement learning"
    - "device placement optimization with reinforcement learning"
    - "towards distributed machine learning in shared clusters a dynamically partitioned approach"
    - "convolutional sequence to sequence learning"

Connected through hyperedge HE-3 (induced by source paper "characterizing application scheduling on edge fog and cloud computing resources"):
  with members:
    - "violet a large scale virtual environment for internet of things"
    - "adaptive energy aware scheduling of dynamic event analytics across edge and cloud resources"
    - "arm wrestling with big data a study of commodity arm64 server for big data workloads"
    - "a manifesto for future generation cloud computing research directions for the next decade"
    - "demystifying fog computing characterizing architectures applications and abstractions"
    - "mobile edge computing a survey on architecture and computation offloading"
    - "big data and fog computing"
    - "distributed scheduling of event analytics across edge and cloud"

Connected through hyperedge HE-4 (induced by source paper "themis fair and efficient gpu cluster scheduling for machine learning workloads"):
  with members:
    - "analysis of large scale multi tenant gpu clusters for dnn training workloads"
    - "attention is all you need"
    - "revisiting distributed synchronous sgd"
    - "efficient hyperparameter optimization and infinitely many armed bandits"
    - "bidirectional attention flow for machine comprehension"
    - "squad 100 000 questions for machine comprehension of text"
    - "wavenet a generative model for raw audio"
    - "google s neural machine translation system bridging the gap between human and machine translation"

Connected through hyperedge HE-5 (induced by source paper "themis fair and efficient gpu cluster scheduling"):
  with members:
    - "analysis of large scale multi tenant gpu clusters for dnn training workloads"
    - "attention is all you need"
    - "revisiting distributed synchronous sgd"
    - "hyperband a novel bandit based approach to hyperparameter optimization"
    - "bidirectional attention flow for machine comprehension"
    - "squad 100 000 questions for machine comprehension of text"
    - "wavenet a generative model for raw audio"
    - "google s neural machine translation system bridging the gap between human and machine translation"

Connected through hyperedge HE-6 (induced by source paper "addressing algorithmic bottlenecks in elastic machine learning with chicle"):
  with members:
    - "elastic cocoa scaling in to improve convergence"
    - "snap ml a hierarchical framework for machine learning"
    - "don t use large mini batches use local sgd"
    - "measuring the effects of data parallelism on neural network training"
    - "scaling sgd batch size to 32k for imagenet training"
    - "understanding and optimizing the performance of distributed machine learning applications on apache spark"
    - "don t decay the learning rate increase the batch size"
    - "large batch training of convolutional networks"

Connected through hyperedge HE-7 (induced by source paper "analysis of large scale multi tenant gpu clusters for dnn training workloads"):
  with members:
    - "superneurons dynamic gpu memory management for training deep neural networks"
    - "attention is all you need"
    - "deepprof performance analysis for deep learning applications via mining gpu execution patterns"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "vdnn virtualized deep neural networks for scalable memory efficient neural network design"
    - "tensorflow a system for large scale machine learning"
    - "deep residual learning for image recognition"
    - "mxnet a flexible and efficient machine learning library for heterogeneous distributed systems"

Connected through hyperedge HE-8 (induced by source paper "ffdl a flexible multi tenant deep learning platform"):
  with members:
    - "horovod fast and easy distributed deep learning in tensorflow"
    - "what does fault tolerant deep learning need from mpi"
    - "hemingway modeling distributed optimization algorithms"
    - "identity mappings in deep residual networks"
    - "rethinking the inception architecture for computer vision"
    - "very deep convolutional networks for large scale image recognition"
    - "caffe convolutional architecture for fast feature embedding"

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)