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: "cocoa a general framework for communication efficient distributed optimization"
  Abstract: The scale of modern datasets necessitates the development of efficient distributed optimization methods for machine learning. We present a general-purpose framework for distributed computing environments, CoCoA, that has an efficient communication scheme and is applicable to a wide variety of problems in machine learning and signal processing. We extend the framework to cover general non-strongly-convex regularizers, including L1-regularized problems like lasso, sparse logistic regression, and elastic net regularization, and show how earlier work can be derived as a special case. We provide convergence guarantees for the class of convex regularized loss minimization objectives, leveraging a novel approach in handling non-strongly-convex regularizers and non-smooth loss functions. The resulting framework has markedly improved performance over state-of-the-art methods, as we illustrate with an extensive set of experiments on real distributed datasets.

Connected through hyperedge HE-1 (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-2 (induced by source paper "exploiting unlabeled data in smart cities using federated learning"):
  with members:
    - "federated learning for edge networks resource optimization and incentive mechanism"
    - "federated learning in mobile edge networks a comprehensive survey"
    - "federated learning strategies for improving communication efficiency"
    - "federated optimization for heterogeneous networks"
    - "don t use large mini batches use local sgd"
    - "federated multi task learning"
    - "communication efficient learning of deep networks from decentralized data"
    - "communication efficient distributed optimization using an approximate newton type method"

Connected through hyperedge HE-3 (induced by source paper "distributed machine learning on mobile devices a survey"):
  with members:
    - "towards federated learning at scale system design"
    - "robust and communication efficient federated learning from non iid data"
    - "on the convergence of adam and beyond"
    - "federated machine learning concept and applications"
    - "secureboost a lossless federated learning framework"
    - "federated learning of out of vocabulary words"
    - "applied federated learning improving google keyboard query suggestions"
    - "federated learning strategies for improving communication efficiency"

Connected through hyperedge HE-4 (induced by source paper "fedpaq a communication efficient federated learning method with periodic averaging and quantization"):
  with members:
    - "distributed federated learning for ultra reliable low latency vehicular communications"
    - "decentralized stochastic optimization and gossip algorithms with compressed communication"
    - "robust federated learning in a heterogeneous environment"
    - "one shot federated learning"
    - "matcha speeding up decentralized sgd via matching decomposition sampling"
    - "robust and communication efficient collaborative learning"
    - "peer to peer federated learning on graphs"
    - "federated learning challenges methods and future directions"

Connected through hyperedge HE-5 (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-6 (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"
    - "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-7 (induced by source paper "clustered federated learning model agnostic distributed multi task optimization under privacy constraints"):
  with members:
    - "deepcabac a universal compression algorithm for deep neural networks"
    - "decentralized stochastic optimization and gossip algorithms with compressed communication"
    - "robust federated learning in a heterogeneous environment"
    - "variational federated multi task learning"
    - "towards federated learning at scale system design"
    - "federated learning challenges methods and future directions"
    - "robust and communication efficient federated learning from non iid data"
    - "federated machine learning concept and applications"

Connected through hyperedge HE-8 (induced by source paper "oversketched newton fast convex optimization for serverless systems"):
  with members:
    - "cloud programming simplified a berkeley view on serverless computing"
    - "harnessing the power of serverless runtimes for large scale optimization"
    - "serverless computing one step forward two steps back"
    - "a distributed second order algorithm you can trust"
    - "speeding up distributed machine learning using codes"
    - "numpywren serverless linear algebra"
    - "oversketch approximate matrix multiplication for the cloud"
    - "giant globally improved approximate newton method for distributed optimization"

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)