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: "communication complexity of distributed convex learning and optimization"
  Abstract: We study the fundamental limits to communication-efficient distributed methods for convex learning and optimization, under different assumptions on the information available to individual machines, and the types of functions considered. We identify cases where existing algorithms are already worst-case optimal, as well as cases where room for further improvement is still possible. Among other things, our results indicate that without similarity between the local objective functions (due to statistical data similarity or otherwise) many communication rounds may be required, even if the machines have unbounded computational power.

Connected through hyperedge HE-1 (induced by source paper "communication optimization strategies for distributed deep learning a survey"):
  with members:
    - "error feedback fixes signsgd and other gradient compression schemes"
    - "powersgd practical low rank gradient compression for distributed optimization"
    - "3lc lightweight and effective traffic compression for distributed machine learning"
    - "priority based parameter propagation for distributed dnn training"
    - "large batch training for lstm and beyond"
    - "scalable deep learning on distributed infrastructures challenges techniques and tools"
    - "yet another accelerated sgd resnet 50 training on imagenet in 74 7 seconds"
    - "redsync reducing synchronization bandwidth for distributed deep learning training system"

Connected through hyperedge HE-2 (induced by source paper "scaffold stochastic controlled averaging for federated learning"):
  with members:
    - "feddane a federated newton type method"
    - "error feedback fixes signsgd and other gradient compression schemes"
    - "distributed learning with compressed gradient differences"
    - "advances and open problems in federated learning"
    - "how good is sgd with random shuffling"
    - "variance reduced local sgd with lower communication complexity"
    - "federated learning of n gram language models"
    - "the error feedback framework better rates for sgd with delayed gradients and compressed communication"

Connected through hyperedge HE-3 (induced by source paper "the communication complexity of optimization"):
  with members:
    - "distributed and streaming linear programming in low dimensions"
    - "solving linear programs in the current matrix multiplication time"
    - "leveraging well conditioned bases streaming distributed summaries in minkowski p norms"
    - "distributed statistical estimation of matrix products with applications"
    - "l1 regression using lewis weights preconditioning and stochastic gradient descent"
    - "on communication complexity of classification problems"
    - "communication optimal distributed clustering"
    - "distributed low rank approximation of implicit functions of a matrix"

Connected through hyperedge HE-4 (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-5 (induced by source paper "the error feedback framework better rates for sgd with delayed gradients and compressed communication"):
  with members:
    - "error feedback fixes signsgd and other gradient compression schemes"
    - "sgd general analysis and improved rates"
    - "unified optimal analysis of the stochastic gradient method"
    - "the complexity of making the gradient small in stochastic convex optimization"
    - "fast and faster convergence of sgd for over parameterized models and an accelerated perceptron"
    - "don t use large mini batches use local sgd"
    - "sparsified sgd with memory"
    - "cooperative sgd a unified framework for the design and analysis of communication efficient sgd algorithms"

Connected through hyperedge HE-6 (induced by source paper "communication efficient distributed learning via lazily aggregated quantized gradients"):
  with members:
    - "error feedback fixes signsgd and other gradient compression schemes"
    - "distributed learning with compressed gradient differences"
    - "efficient decentralized deep learning by dynamic model averaging"
    - "federated learning strategies for improving communication efficiency"
    - "sparsified sgd with memory"
    - "signsgd compressed optimisation for non convex problems"
    - "cooperative sgd a unified framework for the design and analysis of communication efficient sgd algorithms"
    - "error compensated quantized sgd and its applications to large scale distributed optimization"

Connected through hyperedge HE-7 (induced by source paper "model aggregation via good enough model spaces"):
  with members:
    - "the ham10000 dataset a large collection of multi source dermatoscopic images of common pigmented skin lesions"
    - "multi institutional deep learning modeling without sharing patient data a feasibility study on brain tumor segmentation"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "cocoa a general framework for communication efficient distributed optimization"
    - "overcoming catastrophic forgetting in neural networks"
    - "communication efficient learning of deep networks from decentralized data"
    - "going digital a survey on digitalization and large scale data analytics in healthcare"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-8 (induced by source paper "semi cyclic stochastic gradient descent"):
  with members:
    - "towards federated learning at scale system design"
    - "introduction to online convex optimization"
    - "communication efficient learning of deep networks from decentralized data"
    - "federated optimization distributed machine learning for on device intelligence"
    - "a model of inductive bias learning"

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)