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: "priority based parameter propagation for distributed dnn training"
  Abstract: Data parallel training is widely used for scaling distributed deep neural network (DNN) training. However, the performance benefits are often limited by the communication-heavy parameter synchronization step. In this paper, we take advantage of the domain specific knowledge of DNN training and overlap parameter synchronization with computation in order to improve the training performance. We make two key observations: (1) the optimal data representation granularity for the communication may differ from that used by the underlying DNN model implementation and (2) different parameters can afford different synchronization delays. Based on these observations, we propose a new synchronization mechanism called Priority-based Parameter Propagation (P3). P3 synchronizes parameters at a finer granularity and schedules data transmission in such a way that the training process incurs minimal communication delay. We show that P3 can improve the training throughput of ResNet-50, Sockeye and VGG-19 by as much as 25%, 38% and 66% respectively on clusters with realistic network bandwidth

Connected through hyperedge HE-1 (induced by source paper "communication optimization strategies for distributed deep learning a survey"):
  with members:
    - "error feedback fixes signsgd and other gradient compression schemes"
    - "powersgd practical low rank gradient compression for distributed optimization"
    - "3lc lightweight and effective traffic compression for distributed machine learning"
    - "large batch training for lstm and beyond"
    - "scalable deep learning on distributed infrastructures challenges techniques and tools"
    - "yet another accelerated sgd resnet 50 training on imagenet in 74 7 seconds"
    - "redsync reducing synchronization bandwidth for distributed deep learning training system"
    - "local sgd with periodic averaging tighter analysis and adaptive synchronization"

Connected through hyperedge HE-2 (induced by source paper "dynamic parameter allocation in parameter servers"):
  with members:
    - "deep contextualized word representations"
    - "open world knowledge graph completion"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "analogical inference for multi relational embeddings"
    - "holographic embeddings of knowledge graphs"
    - "mxnet a flexible and efficient machine learning library for heterogeneous distributed systems"
    - "one billion word benchmark for measuring progress in statistical language modeling"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-3 (induced by source paper "methods and experiences for developing abstractions for data intensive scientific applications"):
  with members:
    - "learning everywhere pervasive machine learning for effective high performance computation"
    - "performance characterization and modeling of serverless and hpc streaming applications"
    - "synapse synthetic application profiler and emulator"
    - "hadoop on hpc integrating hadoop and pilot based dynamic resource management"
    - "on the hourglass model"
    - "using pilot systems to execute many task workloads on supercomputers"
    - "a comprehensive perspective on pilot job systems"
    - "a tale of two data intensive paradigms applications abstractions and architectures"

Connected through hyperedge HE-4 (induced by source paper "taming unbalanced training workloads in deep learning with partial collective operations"):
  with members:
    - "a modular benchmarking infrastructure for high performance and reproducible deep learning"
    - "demystifying parallel and distributed deep learning an in depth concurrency analysis"
    - "sparcml high performance sparse communication for machine learning"
    - "gossipgrad scalable deep learning using gossip communication based asynchronous gradient descent"
    - "stochastic gradient push for distributed deep learning"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "horovod fast and easy distributed deep learning in tensorflow"
    - "imagenet training in minutes"

Connected through hyperedge HE-5 (induced by source paper "elastic deep learning in multi tenant gpu cluster"):
  with members:
    - "dl2 a deep learning driven scheduler for deep learning clusters"
    - "analysis of large scale multi tenant gpu clusters for dnn training workloads"
    - "horovod fast and easy distributed deep learning in tensorflow"
    - "poseidon an efficient communication architecture for distributed deep learning on gpu clusters"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "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"

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)