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: "parle parallelizing stochastic gradient descent"
  Abstract: We propose a new algorithm called Parle for parallel training of deep networks that converges 2-4x faster than a data-parallel implementation of SGD, while achieving significantly improved error rates that are nearly state-of-the-art on several benchmarks including CIFAR-10 and CIFAR-100, without introducing any additional hyper-parameters. We exploit the phenomenon of flat minima that has been shown to lead to improved generalization error for deep networks. Parle requires very infrequent communication with the parameter server and instead performs more computation on each client, which makes it well-suited to both single-machine, multi-GPU settings and distributed implementations.

Connected through hyperedge HE-1 (induced by source paper "leader stochastic gradient descent for distributed training of deep learning models"):
  with members:
    - "demystifying parallel and distributed deep learning an in depth concurrency analysis"
    - "understanding the loss surface of neural networks for binary classification"
    - "horovod fast and easy distributed deep learning in tensorflow"
    - "complete dictionary recovery over the sphere i overview and the geometric picture"
    - "integrated model batch and domain parallelism in training neural networks"
    - "scaling sgd batch size to 32k for imagenet training"
    - "the power of interpolation understanding the effectiveness of sgd in modern over parametrized learning"
    - "no spurious local minima in nonconvex low rank problems a unified geometric analysis"

Connected through hyperedge HE-2 (induced by source paper "adaptive communication strategies to achieve the best error runtime trade off in local update sgd"):
  with members:
    - "don t use large mini batches use local sgd"
    - "on the convergence properties of a k step averaging stochastic gradient descent algorithm for nonconvex optimization"
    - "cooperative sgd a unified framework for the design and analysis of communication efficient sgd algorithms"
    - "pipedream fast and efficient pipeline parallel dnn training"
    - "don t decay the learning rate increase the batch size"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "federated multi task learning"
    - "coupling adaptive batch sizes with learning rates"

Connected through hyperedge HE-3 (induced by source paper "cooperative sgd a unified framework for the design and analysis of communication efficient sgd algorithms"):
  with members:
    - "don t use large mini batches use local sgd"
    - "on the convergence properties of a k step averaging stochastic gradient descent algorithm for nonconvex optimization"
    - "signsgd compressed optimisation for non convex problems"
    - "deep gradient compression reducing the communication bandwidth for distributed training"
    - "gradient sparsification for communication efficient distributed optimization"
    - "terngrad ternary gradients to reduce communication in distributed deep learning"
    - "cocoa a general framework for communication efficient distributed optimization"
    - "how to scale distributed deep learning"

Connected through hyperedge HE-4 (induced by source paper "stochastic gradient descent performs variational inference converges to limit cycles for deep networks"):
  with members:
    - "non convex learning via stochastic gradient langevin dynamics a nonasymptotic analysis"
    - "geometry of optimization and implicit regularization in deep learning"
    - "emergence of invariance and disentanglement in deep representations"
    - "opening the black box of deep neural networks via information"
    - "deep relaxation partial differential equations for optimizing deep neural networks"
    - "on the emergence of invariance and disentangling in deep representations"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "smash one shot model architecture search through hypernetworks"

Connected through hyperedge HE-5 (induced by source paper "deep relaxation partial differential equations for optimizing deep neural networks"):
  with members:
    - "stochastic gradient descent performs variational inference converges to limit cycles for deep networks"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "entropy sgd biasing gradient descent into wide valleys"
    - "identity mappings in deep residual networks"
    - "noisy activation functions"
    - "training recurrent neural networks by diffusion"
    - "trivializing the energy landscape of deep networks"
    - "on the energy landscape of deep networks"

Connected through hyperedge HE-6 (induced by source paper "what does fault tolerant deep learning need from mpi"):
  with members:
    - "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"
    - "asynchronous parallel stochastic gradient descent a numeric core for scalable distributed machine learning algorithms"
    - "poseidon a system architecture for efficient gpu based deep learning on multiple machines"

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)