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: "parallel training of dnns with natural gradient and parameter averaging"
  Abstract: We describe the neural-network training framework used in the Kaldi speech recognition toolkit, which is geared towards training DNNs with large amounts of training data using multiple GPU-equipped or multi-core machines. In order to be as hardware-agnostic as possible, we needed a way to use multiple machines without generating excessive network traffic. Our method is to average the neural network parameters periodically (typically every minute or two), and redistribute the averaged parameters to the machines for further training. Each machine sees different data. By itself, this method does not work very well. However, we have another method, an approximate and efficient implementation of Natural Gradient for Stochastic Gradient Descent (NG-SGD), which seems to allow our periodic-averaging method to work well, as well as substantially improving the convergence of SGD on a single machine.

Connected through hyperedge HE-1 (induced by source paper "variational federated multi task learning"):
  with members:
    - "federated collaborative filtering for privacy preserving personalized recommendation system"
    - "oracle order robust adaptive continual learning"
    - "knowledge transfer in deep block modular neural networks"
    - "applied federated learning improving google keyboard query suggestions"
    - "flipout efficient pseudo independent weight perturbations on mini batches"
    - "federated optimization for heterogeneous networks"
    - "leaf a benchmark for federated settings"
    - "communication efficient on device machine learning federated distillation and augmentation under non iid private data"

Connected through hyperedge HE-2 (induced by source paper "local sgd with periodic averaging tighter analysis and adaptive synchronization"):
  with members:
    - "recycled admm improving the privacy and accuracy of distributed algorithms"
    - "efficient decentralized deep learning by dynamic model averaging"
    - "adaptive communication strategies to achieve the best error runtime trade off in local update sgd"
    - "improving the privacy and accuracy of admm based distributed algorithms"
    - "don t use large mini batches use local sgd"
    - "sparsified sgd with memory"
    - "on the convergence properties of a k step averaging stochastic gradient descent algorithm for nonconvex optimization"
    - "signsgd compressed optimisation for non convex problems"

Connected through hyperedge HE-3 (induced by source paper "variance reduced local sgd with lower communication complexity"):
  with members:
    - "error feedback fixes signsgd and other gradient compression schemes"
    - "advances and open problems in federated learning"
    - "federated learning challenges methods and future directions"
    - "faster distributed deep net training computation and communication decoupled stochastic gradient descent"
    - "signsgd with majority vote is communication efficient and fault tolerant"
    - "federated learning strategies for improving communication efficiency"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "on the convergence of local descent methods in federated learning"):
  with members:
    - "scaffold stochastic controlled averaging for on device federated learning"
    - "one shot federated learning"
    - "federated learning challenges methods and future directions"
    - "agnostic federated learning"
    - "first analysis of local gd on heterogeneous data"
    - "local sgd with periodic averaging tighter analysis and adaptive synchronization"
    - "fair resource allocation in federated learning"
    - "efficient decentralized deep learning by dynamic model averaging"

Connected through hyperedge HE-5 (induced by source paper "a random gossip bmuf process for neural language modeling"):
  with members:
    - "transformer xl attentive language models beyond a fixed length context"
    - "demystifying parallel and distributed deep learning an in depth concurrency analysis"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "horovod fast and easy distributed deep learning in tensorflow"
    - "attention is all you need"
    - "empirical evaluation of parallel training algorithms on acoustic modeling"
    - "efficient softmax approximation for gpus"
    - "how to scale distributed deep learning"

Connected through hyperedge HE-6 (induced by source paper "a multi modal feature embedding approach to diagnose alzheimer disease from spoken language"):
  with members:
    - "deep learning for sensor based activity recognition a survey"
    - "isolating effects of age with fair representation learning when assessing dementia"
    - "the effect of heterogeneous data for alzheimer s disease detection from speech"
    - "end to end text dependent speaker verification"

Connected through hyperedge HE-7 (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"
    - "parle parallelizing stochastic gradient descent"
    - "terngrad ternary gradients to reduce communication in distributed deep learning"
    - "cocoa a general framework for communication efficient distributed optimization"

Connected through hyperedge HE-8 (induced by source paper "the capio 2017 conversational speech recognition system"):
  with members:
    - "english conversational telephone speech recognition by humans and machines"
    - "residual lstm design of a deep recurrent architecture for distant speech recognition"
    - "the microsoft 2017 conversational speech recognition system"
    - "an improved residual lstm architecture for acoustic modeling"
    - "language modeling with highway lstm"
    - "comparing human and machine errors in conversational speech transcription"
    - "sequence training and adaptation of highway deep neural networks"
    - "densely connected convolutional networks"

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.NE(Neural and Evolutionary Computing)