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: "the convergence of stochastic gradient descent in asynchronous shared memory"
  Abstract: Stochastic Gradient Descent (SGD) is a fundamental algorithm in machine learning, representing the optimization backbone for training several classic models, from regression to neural networks. Given the recent practical focus on distributed machine learning, significant work has been dedicated to the convergence properties of this algorithm under the inconsistent and noisy updates arising from execution in a distributed environment. However, surprisingly, the convergence properties of this classic algorithm in the standard shared-memory model are still not well-understood. #R##N#In this work, we address this gap, and provide new convergence bounds for lock-free concurrent stochastic gradient descent, executing in the classic asynchronous shared memory model, against a strong adaptive adversary. Our results give improved upper and lower bounds on the "price of asynchrony" when executing the fundamental SGD algorithm in a concurrent setting. They show that this classic optimization tool can converge faster and with a wider range of parameters than previously known under asynchronous iterations. At the same time, we exhibit a fundamental trade-off between the maximum delay in the system and the rate at which SGD can converge, which governs the set of parameters under which this algorithm can still work efficiently.

Connected through hyperedge HE-1 (induced by source paper "elastic consistency a general consistency model for distributed stochastic gradient descent"):
  with members:
    - "error feedback fixes signsgd and other gradient compression schemes"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "communication compression for decentralized training"
    - "fault tolerance in iterative convergent machine learning"
    - "sparsified sgd with memory"
    - "toward understanding the impact of staleness in distributed machine learning"
    - "gradient sparsification for communication efficient distributed optimization"
    - "sparse communication for distributed gradient descent"

Connected through hyperedge HE-2 (induced by source paper "distributed hierarchical gpu parameter server for massive scale deep learning ads systems"):
  with members:
    - "bandana using non volatile memory for storing deep learning models"
    - "parameter hub a rack scale parameter server for distributed deep neural network training"
    - "the case for learned index structures"
    - "poseidon an efficient communication architecture for distributed deep learning on gpu clusters"
    - "product based neural networks for user response prediction"

Connected through hyperedge HE-3 (induced by source paper "communication trade offs for synchronized distributed sgd with large step size"):
  with members:
    - "federated learning strategies for improving communication efficiency"
    - "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"
    - "on large batch training for deep learning generalization gap and sharp minima"

Connected through hyperedge HE-4 (induced by source paper "robust learning from untrusted sources"):
  with members:
    - "byzantine stochastic gradient descent"
    - "algorithms and theory for multiple source adaptation"
    - "byzantine robust distributed learning towards optimal statistical rates"
    - "improving the generalization of adversarial training with domain adaptation"
    - "mitigating sybils in federated learning poisoning"
    - "using trusted data to train deep networks on labels corrupted by severe noise"
    - "on fundamental limits of robust learning"
    - "learning discrete distributions from untrusted batches"

Connected through hyperedge HE-5 (induced by source paper "band limited training and inference for convolutional neural networks"):
  with members:
    - "training behavior of deep neural network in frequency domain"
    - "amc automl for model compression and acceleration on mobile devices"
    - "high accuracy low precision training"
    - "training deep neural networks with 8 bit floating point numbers"
    - "efficient sparse winograd convolutional neural networks"
    - "fft convolutions are faster than winograd on modern cpus here is why"
    - "adversarial attacks on neural network policies"
    - "foolbox v0 8 0 a python toolbox to benchmark the robustness of machine learning models"

Connected through hyperedge HE-6 (induced by source paper "popsgd decentralized stochastic gradient descent in the population model"):
  with members:
    - "decentralized stochastic optimization and gossip algorithms with compressed communication"
    - "decentralization meets quantization"
    - "stochastic gradient push for distributed deep learning"
    - "scaling sgd batch size to 32k for imagenet training"
    - "sparse communication for distributed gradient descent"
    - "terngrad ternary gradients to reduce communication in distributed deep learning"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "space optimal majority in population protocols"

Connected through hyperedge HE-7 (induced by source paper "mindthestep asyncpsgd adaptive asynchronous parallel stochastic gradient descent"):
  with members:
    - "towards understanding acceleration tradeoff between momentum and asynchrony in nonconvex stochastic optimization"
    - "systematic evaluation of convolution neural network advances on the imagenet"
    - "terngrad ternary gradients to reduce communication in distributed deep learning"
    - "qsgd communication efficient sgd via gradient quantization and encoding"
    - "an overview of gradient descent optimization algorithms"
    - "tensorflow a system for large scale machine learning"
    - "on large batch training for deep learning generalization gap and sharp minima"
    - "staleness aware async sgd for distributed deep learning"

Connected through hyperedge HE-8 (induced by source paper "don t use large mini batches use local sgd"):
  with members:
    - "error feedback fixes signsgd and other gradient compression schemes"
    - "advances and open problems in federated learning"
    - "communication trade offs for synchronized distributed sgd with large step size"
    - "interplay between optimization and generalization of stochastic gradient descent with covariance noise"
    - "on the ineffectiveness of variance reduced optimization for deep learning"
    - "federated learning strategies for improving communication efficiency"
    - "an empirical model of large batch training"
    - "adaptive communication strategies to achieve the best error runtime trade off in local update sgd"

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)