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: "firecaffe near linear acceleration of deep neural network training on compute clusters"
  Abstract: Long training times for high-accuracy deep neural networks (DNNs) impede research into new DNN architectures and slow the development of high-accuracy DNNs. In this paper we present FireCaffe, which successfully scales deep neural network training across a cluster of GPUs. We also present a number of best practices to aid in comparing advancements in methods for scaling and accelerating the training of deep neural networks. The speed and scalability of distributed algorithms is almost always limited by the overhead of communicating between servers; DNN training is not an exception to this rule. Therefore, the key consideration here is to reduce communication overhead wherever possible, while not degrading the accuracy of the DNN models that we train. Our approach has three key pillars. First, we select network hardware that achieves high bandwidth between GPU servers -- Infiniband or Cray interconnects are ideal for this. Second, we consider a number of communication algorithms, and we find that reduction trees are more efficient and scalable than the traditional parameter server approach. Third, we optionally increase the batch size to reduce the total quantity of communication during DNN training, and we identify hyperparameters that allow us to reproduce the small-batch accuracy while training with large batch sizes. When training GoogLeNet and Network-in-Network on ImageNet, we achieve a 47x and 39x speedup, respectively, when training on a cluster of 128 GPUs.

Connected through hyperedge HE-1 (induced by source paper "pointhop a lightweight learning model on point sets for 3d classification"):
  with members:
    - "pointhop an explainable machine learning method for point cloud classification"
    - "interpretable convolutional neural networks via feedforward design"
    - "meshnet mesh neural network for 3d shape representation"
    - "dynamic graph cnn for learning on point clouds"
    - "on data driven saak transform"
    - "voxelnet end to end learning for point cloud based 3d object detection"
    - "a saak transform approach to efficient scalable and robust handwritten digits recognition"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"

Connected through hyperedge HE-2 (induced by source paper "large batch training for lstm and beyond"):
  with members:
    - "image classification at supercomputer scale"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "mesh tensorflow deep learning for supercomputers"
    - "highly scalable deep learning training system with mixed precision training imagenet in four minutes"
    - "measuring the effects of data parallelism on neural network training"
    - "large scale distributed second order optimization using kronecker factored approximate curvature for deep convolutional neural networks"
    - "imagenet training in minutes"
    - "in datacenter performance analysis of a tensor processing unit"

Connected through hyperedge HE-3 (induced by source paper "etalumis bringing probabilistic programming to scientific simulators at scale"):
  with members:
    - "massively distributed sgd imagenet resnet 50 training in a flash"
    - "pyro deep universal probabilistic programming"
    - "an empirical model of large batch training"
    - "measuring the effects of data parallelism on neural network training"
    - "a comprehensive study of batch construction strategies for recurrent neural networks in mxnet"
    - "scaling sgd batch size to 32k for imagenet training"
    - "carla an open urban driving simulator"
    - "improvements to inference compilation for probabilistic programming in large scale scientific simulators"

Connected through hyperedge HE-4 (induced by source paper "reducing bert pre training time from 3 days to 76 minutes"):
  with members:
    - "large batch training for lstm and beyond"
    - "yet another accelerated sgd resnet 50 training on imagenet in 74 7 seconds"
    - "massively distributed sgd imagenet resnet 50 training in a flash"
    - "image classification at supercomputer scale"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "highly scalable deep learning training system with mixed precision training imagenet in four minutes"
    - "measuring the effects of data parallelism on neural network training"
    - "large scale distributed second order optimization using kronecker factored approximate curvature for deep convolutional neural networks"

Connected through hyperedge HE-5 (induced by source paper "a crowdsourcing framework for on device federated learning"):
  with members:
    - "distributed federated learning for ultra reliable low latency vehicular communications"
    - "federated learning over wireless networks convergence analysis and resource allocation"
    - "on device federated learning via blockchain and its latency analysis"
    - "federated learning strategies for improving communication efficiency"
    - "online job scheduling in distributed machine learning clusters"
    - "federated optimization for heterogeneous networks"
    - "adaptive communication strategies to achieve the best error runtime trade off in local update sgd"
    - "beyond inferring class representatives user level privacy leakage from federated learning"

Connected through hyperedge HE-6 (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"
    - "parameter hub a rack scale parameter server for distributed deep neural network training"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "deep leakage from gradients"):
  with members:
    - "towards federated learning at scale system design"
    - "exploiting unintended feature leakage in collaborative learning"
    - "federated learning strategies for improving communication efficiency"
    - "variance based gradient compression for efficient distributed deep learning"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "highly scalable deep learning training system with mixed precision training imagenet in four minutes"
    - "horovod fast and easy distributed deep learning in tensorflow"
    - "deep gradient compression reducing the communication bandwidth for distributed training"

Connected through hyperedge HE-8 (induced by source paper "speeding up deep learning with transient servers"):
  with members:
    - "demystifying parallel and distributed deep learning an in depth concurrency analysis"
    - "imagenet training in minutes"
    - "deep gradient compression reducing the communication bandwidth for distributed training"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "extremely large minibatch sgd training resnet 50 on imagenet in 15 minutes"
    - "performance modeling and evaluation of distributed deep learning frameworks on gpus"
    - "portfolio driven resource management for transient cloud servers"
    - "revisiting distributed synchronous sgd"

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.CV(Computer Vision and Pattern Recognition)