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: "high performance distributed ml at scale through parameter server consistency models"
  Abstract: As Machine Learning (ML) applications increase in data size and model complexity, practitioners turn to distributed clusters to satisfy the increased computational and memory demands. Unfortunately, effective use of clusters for ML requires considerable expertise in writing distributed code, while highly-abstracted frameworks like Hadoop have not, in practice, approached the performance seen in specialized ML implementations. The recent Parameter Server (PS) paradigm is a middle ground between these extremes, allowing easy conversion of single-machine parallel ML applications into distributed ones, while maintaining high throughput through relaxed "consistency models" that allow inconsistent parameter reads. However, due to insufficient theoretical study, it is not clear which of these consistency models can really ensure correct ML algorithm output; at the same time, there remain many theoretically-motivated but undiscovered opportunities to maximize computational throughput. Motivated by this challenge, we study both the theoretical guarantees and empirical behavior of iterative-convergent ML algorithms in existing PS consistency models. We then use the gleaned insights to improve a consistency model using an "eager" PS communication mechanism, and implement it as a new PS system that enables ML algorithms to reach their solution more quickly.

Connected through hyperedge HE-1 (induced by source paper "machine learning systems for highly distributed and rapidly growing data"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "semi cyclic stochastic gradient descent"
    - "the non iid data quagmire of decentralized machine learning"
    - "enabling the adoption of processing in memory challenges mechanisms future research directions"
    - "group normalization"
    - "federated learning with non iid data"
    - "deep gradient compression reducing the communication bandwidth for distributed training"
    - "accurate large minibatch sgd training imagenet in 1 hour"

Connected through hyperedge HE-2 (induced by source paper "dynamic stale synchronous parallel distributed training for deep learning"):
  with members:
    - "the effectiveness of data augmentation in image classification using deep learning"
    - "revisiting distributed synchronous sgd"
    - "omnivore an optimizer for multi device deep learning on cpus and gpus"
    - "mllib machine learning in apache spark"
    - "deep residual learning for image recognition"
    - "mxnet a flexible and efficient machine learning library for heterogeneous distributed systems"
    - "rethinking the inception architecture for computer vision"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-3 (induced by source paper "hop heterogeneity aware decentralized training"):
  with members:
    - "communication compression for decentralized training"
    - "revisiting distributed synchronous sgd"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "asap asynchronous approximate data parallel computation"
    - "tensorflow a system for large scale machine learning"
    - "mxnet a flexible and efficient machine learning library for heterogeneous distributed systems"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-4 (induced by source paper "parameter hub a rack scale parameter server for distributed deep neural network training"):
  with members:
    - "horovod fast and easy distributed deep learning in tensorflow"
    - "imagenet training in minutes"
    - "deep gradient compression reducing the communication bandwidth for distributed training"
    - "revisiting distributed synchronous sgd"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "aggregated residual transformations for deep neural networks"
    - "benchmarking state of the art deep learning software tools"
    - "inception v4 inception resnet and the impact of residual connections on learning"

Connected through hyperedge HE-5 (induced by source paper "fault tolerance in iterative convergent machine learning"):
  with members:
    - "poseidon an efficient communication architecture for distributed deep learning on gpu clusters"
    - "distributed statistical machine learning in adversarial settings byzantine gradient descent"
    - "how to escape saddle points efficiently"
    - "tensorflow a system for large scale machine learning"
    - "escaping from saddle points online stochastic gradient for tensor decomposition"
    - "adam a method for stochastic optimization"
    - "making gradient descent optimal for strongly convex stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "toward understanding the impact of staleness in distributed machine learning"):
  with members:
    - "revisiting small batch training for deep neural networks"
    - "omnivore an optimizer for multi device deep learning on cpus and gpus"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "on the performance and convergence of distributed stream processing via approximate fault tolerance"):
  with members:
    - "mllib machine learning in apache spark"
    - "blinkdb queries with bounded errors and bounded response times on very large data"

Connected through hyperedge HE-8 (induced by source paper "poseidon an efficient communication architecture for distributed deep learning on gpu clusters"):
  with members:
    - "revisiting distributed synchronous sgd"
    - "recurrent topic transition gan for visual paragraph generation"
    - "tensorflow a system for large scale machine learning"
    - "poseidon a system architecture for efficient gpu based deep learning on multiple machines"
    - "deep residual learning for image recognition"
    - "firecaffe near linear acceleration of deep neural network training on compute clusters"
    - "mxnet a flexible and efficient machine learning library for heterogeneous distributed systems"
    - "rethinking the inception architecture for computer vision"

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)