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: "parameter hub a rack scale parameter server for distributed deep neural network training"
  Abstract: Distributed deep neural network (DDNN) training constitutes an increasingly important workload that frequently runs in the cloud. Larger DNN models and faster compute engines are shifting DDNN training bottlenecks from computation to communication. This paper characterizes DDNN training to precisely pinpoint these bottlenecks. We found that timely training requires high performance parameter servers (PSs) with optimized network stacks and gradient processing pipelines, as well as server and network hardware with balanced computation and communication resources. We therefore propose PHub, a high performance multi-tenant, rack-scale PS design. PHub co-designs the PS software and hardware to accelerate rack-level and hierarchical cross-rack parameter exchange, with an API compatible with many DDNN training frameworks. PHub provides a performance improvement of up to 2.7x compared to state-of-the-art distributed training techniques for cloud-based ImageNet workloads, with 25% better throughput per dollar.

Connected through hyperedge HE-1 (induced by source paper "distributed hierarchical gpu parameter server for massive scale deep learning ads systems"):
  with members:
    - "the convergence of stochastic gradient descent in asynchronous shared memory"
    - "bandana using non volatile memory for storing deep learning models"
    - "the case for learned index structures"
    - "poseidon an efficient communication architecture for distributed deep learning on gpu clusters"
    - "product based neural networks for user response prediction"

Connected through hyperedge HE-2 (induced by source paper "accelerated training for cnn distributed deep learning through automatic resource aware layer placement"):
  with members:
    - "deep learning based recommender system a survey and new perspectives"
    - "beyond data and model parallelism for deep neural networks"
    - "gpipe efficient training of giant neural networks using pipeline parallelism"
    - "horovod fast and easy distributed deep learning in tensorflow"
    - "pipedream fast and efficient pipeline parallel dnn training"
    - "exploring hidden dimensions in parallelizing convolutional neural networks"
    - "gradient sparsification for communication efficient distributed optimization"
    - "poseidon an efficient communication architecture for distributed deep learning on gpu clusters"

Connected through hyperedge HE-3 (induced by source paper "heterogeneity aware asynchronous decentralized training"):
  with members:
    - "supporting very large models using automatic dataflow graph partitioning"
    - "hop heterogeneity aware decentralized training"
    - "yet another accelerated sgd resnet 50 training on imagenet in 74 7 seconds"
    - "optimizing network performance for distributed dnn training on gpu clusters imagenet alexnet training in 1 5 minutes"
    - "pipe sgd a decentralized pipelined sgd framework for distributed deep net training"
    - "demystifying parallel and distributed deep learning an in depth concurrency analysis"
    - "communication compression for decentralized training"
    - "beyond data and model parallelism for deep neural networks"

Connected through hyperedge HE-4 (induced by source paper "optimizing network performance for distributed dnn training on gpu clusters imagenet alexnet training in 1 5 minutes"):
  with members:
    - "massively distributed sgd imagenet resnet 50 training in a flash"
    - "highly scalable deep learning training system with mixed precision training imagenet in four minutes"
    - "tictac accelerating distributed deep learning with communication scheduling"
    - "horovod fast and easy distributed deep learning in tensorflow"
    - "imagenet training in minutes"
    - "deep gradient compression reducing the communication bandwidth for distributed training"
    - "poseidon an efficient communication architecture for distributed deep learning on gpu clusters"
    - "don t decay the learning rate increase the batch size"

Connected through hyperedge HE-5 (induced by source paper "priority based parameter propagation for distributed dnn training"):
  with members:
    - "gossipgrad scalable deep learning using gossip communication based asynchronous gradient descent"
    - "tbd benchmarking and analyzing deep neural network training"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "deep gradient compression reducing the communication bandwidth for distributed training"
    - "adacomp adaptive residual gradient compression for data parallel distributed training"
    - "sockeye a toolkit for neural machine translation"
    - "poseidon an efficient communication architecture for distributed deep learning on gpu clusters"
    - "performance modeling and evaluation of distributed deep learning frameworks on gpus"

Connected through hyperedge HE-6 (induced by source paper "chasing similarity distribution aware aggregation scheduling extended version"):
  with members:
    - "moment based quantile sketches for efficient high cardinality aggregation queries"
    - "ups and downs modeling the visual evolution of fashion trends with one class collaborative filtering"
    - "bayesian locality sensitive hashing for fast similarity search"
    - "reductions between expansion problems"

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)