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: "horovod fast and easy distributed deep learning in tensorflow"
  Abstract: Training modern deep learning models requires large amounts of computation, often provided by GPUs. Scaling computation from one GPU to many can enable much faster training and research progress but entails two complications. First, the training library must support inter-GPU communication. Depending on the particular methods employed, this communication may entail anywhere from negligible to significant overhead. Second, the user must modify his or her training code to take advantage of inter-GPU communication. Depending on the training library's API, the modification required may be either significant or minimal. #R##N#Existing methods for enabling multi-GPU training under the TensorFlow library entail non-negligible communication overhead and require users to heavily modify their model-building code, leading many researchers to avoid the whole mess and stick with slower single-GPU training. In this paper we introduce Horovod, an open source library that improves on both obstructions to scaling: it employs efficient inter-GPU communication via ring reduction and requires only a few lines of modification to user code, enabling faster, easier distributed training in TensorFlow. Horovod is available under the Apache 2.0 license at this https URL

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"
    - "priority based parameter propagation for distributed dnn training"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "stochastic weight averaging in parallel large batch training that generalizes well"):
  with members:
    - "stochastic gradient methods with layer wise adaptive moments for training of deep networks"
    - "swalp stochastic weight averaging in low precision training"
    - "federated learning challenges methods and future directions"
    - "a simple baseline for bayesian uncertainty in deep learning"
    - "there are many consistent explanations of unlabeled data why you should average"
    - "an empirical model of large batch training"
    - "don t use large mini batches use local sgd"
    - "averaging weights leads to wider optima and better generalization"

Connected through hyperedge HE-3 (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"
    - "tune a research platform for distributed model selection and training"
    - "ray a distributed framework for emerging ai applications"
    - "rllib abstractions for distributed reinforcement learning"
    - "proximal policy optimization algorithms"

Connected through hyperedge HE-4 (induced by source paper "communication contention aware scheduling of multiple deep learning training jobs"):
  with members:
    - "a distributed synchronous sgd algorithm with global top k sparsification for low bandwidth networks"
    - "analysis of large scale multi tenant gpu clusters for dnn training workloads"
    - "low latency job scheduling with preemption for the development of deep learning"
    - "tony an orchestrator for distributed machine learning jobs"
    - "online job scheduling in distributed machine learning clusters"
    - "highly scalable deep learning training system with mixed precision training imagenet in four minutes"
    - "deep gradient compression reducing the communication bandwidth for distributed training"
    - "scaling deep learning on gpu and knights landing clusters"

Connected through hyperedge HE-5 (induced by source paper "stannis low power acceleration of deep neural network training using computational storage"):
  with members:
    - "age of information in multiple sensing"
    - "benchmarking tpu gpu and cpu platforms for deep learning"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "tensorflow a system for large scale machine learning"
    - "deep residual learning for image recognition"
    - "a reliable effective terascale linear learning system"

Connected through hyperedge HE-6 (induced by source paper "polytransform deep polygon transformer for instance segmentation"):
  with members:
    - "darnet deep active ray network for building segmentation"
    - "panoptic segmentation"
    - "video instance segmentation"
    - "devil is in the edges learning semantic boundaries from noisy annotations"
    - "adaptis adaptive instance selection network"
    - "mask scoring r cnn"
    - "ssap single shot instance segmentation with affinity pyramid"
    - "hybrid task cascade for instance segmentation"

Connected through hyperedge HE-7 (induced by source paper "distributed learning of deep neural networks using independent subnet training"):
  with members:
    - "powersgd practical low rank gradient compression for distributed optimization"
    - "mlsys the new frontier of machine learning systems"
    - "large batch training for lstm and beyond"
    - "communication efficient distributed sgd with sketching"
    - "reducing bert pre training time from 3 days to 76 minutes"
    - "inefficiency of k fac for large batch size training"
    - "survey of dropout methods for deep neural networks"
    - "on the ineffectiveness of variance reduced optimization for deep learning"

Connected through hyperedge HE-8 (induced by source paper "simulation based reinforcement learning for real world autonomous driving"):
  with members:
    - "model free deep reinforcement learning for urban autonomous driving"
    - "generalization through simulation integrating simulated and real data into deep reinforcement learning for vision based autonomous flight"
    - "model based reinforcement learning for atari"
    - "divis domain invariant visual servoing for collision free goal reaching"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "the limits and potentials of deep learning for robotics"
    - "learning dexterous in hand manipulation"
    - "sim to real transfer of robotic control with dynamics randomization"

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)