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: "stochastic distributed and federated optimization for machine learning"
  Abstract: We study optimization algorithms for the finite sum problems frequently arising in machine learning applications. First, we propose novel variants of stochastic gradient descent with a variance reduction property that enables linear convergence for strongly convex objectives. Second, we study distributed setting, in which the data describing the optimization problem does not fit into a single computing node. In this case, traditional methods are inefficient, as the communication costs inherent in distributed optimization become the bottleneck. We propose a communication-efficient framework which iteratively forms local subproblems that can be solved with arbitrary local optimization algorithms. Finally, we introduce the concept of Federated Optimization/Learning, where we try to solve the machine learning problems without having data stored in any centralized manner. The main motivation comes from industry when handling user-generated data. The current prevalent practice is that companies collect vast amounts of user data and store them in datacenters. An alternative we propose is not to collect the data in first place, and instead occasionally use the computational power of users' devices to solve the very same optimization problems, while alleviating privacy concerns at the same time. In such setting, minimization of communication rounds is the primary goal, and we demonstrate that solving the optimization problems in such circumstances is conceptually tractable.

Connected through hyperedge HE-1 (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-2 (induced by source paper "data encoding for byzantine resilient distributed optimization"):
  with members:
    - "speeding up distributed machine learning using codes"
    - "byzantine robust distributed learning towards optimal statistical rates"
    - "short dot computing large linear transforms distributedly using coded short dot products"
    - "distributed statistical machine learning in adversarial settings byzantine gradient descent"
    - "an equivalence between the lasso and support vector machines"
    - "making gradient descent optimal for strongly convex stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "qsgd communication efficient sgd via gradient quantization and encoding"):
  with members:
    - "terngrad ternary gradients to reduce communication in distributed deep learning"
    - "dorefa net training low bitwidth convolutional neural networks with low bitwidth gradients"
    - "distributed mean estimation with limited communication"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "communication complexity of distributed convex learning and 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)