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: "asynchronous parallel stochastic gradient descent a numeric core for scalable distributed machine learning algorithms"
  Abstract: The implementation of a vast majority of machine learning (ML) algorithms boils down to solving a numerical optimization problem. In this context, Stochastic Gradient Descent (SGD) methods have long proven to provide good results, both in terms of convergence and accuracy. Recently, several parallelization approaches have been proposed in order to scale SGD to solve very large ML problems. At their core, most of these approaches are following a map-reduce scheme. This paper presents a novel parallel updating algorithm for SGD, which utilizes the asynchronous single-sided communication paradigm. Compared to existing methods, Asynchronous Parallel Stochastic Gradient Descent (ASGD) provides faster (or at least equal) convergence, close to linear scaling and stable accuracy.

Connected through hyperedge HE-1 (induced by source paper "communication trade offs for synchronized distributed sgd with large step size"):
  with members:
    - "federated learning strategies for improving communication efficiency"
    - "the convergence of stochastic gradient descent in asynchronous shared memory"
    - "terngrad ternary gradients to reduce communication in distributed deep learning"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "cocoa a general framework for communication efficient distributed optimization"
    - "communication lower bounds for distributed convex optimization partition data on features"
    - "the zipml framework for training models with end to end low precision the cans the cannots and a little bit of deep learning"
    - "federated learning of deep networks using model averaging"

Connected through hyperedge HE-2 (induced by source paper "gradient sparification for asynchronous distributed training"):
  with members:
    - "doublesqueeze parallel stochastic gradient descent with double pass error compensated compression"
    - "sparcml high performance sparse communication for machine learning"
    - "highly scalable deep learning training system with mixed precision training imagenet in four minutes"
    - "deep gradient compression reducing the communication bandwidth for distributed training"
    - "adacomp adaptive residual gradient compression for data parallel distributed training"
    - "scaling sgd batch size to 32k for imagenet training"
    - "stochastic nonconvex optimization with large minibatches"
    - "sparse communication for distributed gradient descent"

Connected through hyperedge HE-3 (induced by source paper "dc s3gd delay compensated stale synchronous sgd for large scale decentralized neural network training"):
  with members:
    - "demystifying parallel and distributed deep learning an in depth concurrency analysis"
    - "an empirical model of large batch training"
    - "averaging weights leads to wider optima and better generalization"
    - "highly scalable deep learning training system with mixed precision training imagenet in four minutes"
    - "large scale distributed second order optimization using kronecker factored approximate curvature for deep convolutional neural networks"
    - "imagenet training in minutes"
    - "scaling sgd batch size to 32k for imagenet training"
    - "visualizing the loss landscape of neural nets"

Connected through hyperedge HE-4 (induced by source paper "distributed deep reinforcement learning learn how to play atari games in 21 minutes"):
  with members:
    - "accelerated methods for deep reinforcement learning"
    - "imagenet training in minutes"
    - "scaling sgd batch size to 32k for imagenet training"
    - "emergent complexity via multi agent competition"
    - "sparse communication for distributed gradient descent"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "revisiting distributed synchronous sgd"
    - "atari games and intel processors"

Connected through hyperedge HE-5 (induced by source paper "how much are you willing to share a poker styled selective privacy preserving framework for recommender systems"):
  with members:
    - "matrix completion has no spurious local minimum"
    - "federated optimization distributed machine learning for on device intelligence"
    - "image based recommendations on styles and substitutes"
    - "mllib machine learning in apache spark"
    - "parallel and distributed collaborative filtering a survey"

Connected through hyperedge HE-6 (induced by source paper "what does fault tolerant deep learning need from mpi"):
  with members:
    - "parle parallelizing stochastic gradient descent"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "distributed deep learning using synchronous stochastic gradient descent"
    - "fault tolerant frequent pattern mining"
    - "accelerating deep learning with shrinkage and recall"
    - "adaptive neuron apoptosis for accelerating deep learning on large scale systems"
    - "application of deep convolutional neural networks for detecting extreme weather in climate datasets"
    - "poseidon a system architecture for efficient gpu based deep learning on multiple machines"

Connected through hyperedge HE-7 (induced by source paper "using gpi 2 for distributed memory paralleliziation of the caffe toolbox to speed up deep neural network training"):
  with members:
    - "going deeper with convolutions"
    - "caffe convolutional architecture for fast feature embedding"

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)