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: "measuring the effects of non identical data distribution for federated visual classification"
  Abstract: Federated Learning enables visual models to be trained in a privacy-preserving way using real-world data from mobile devices. Given their distributed nature, the statistics of the data across these devices is likely to differ significantly. In this work, we look at the effect such non-identical data distributions has on visual classification via Federated Learning. We propose a way to synthesize datasets with a continuous range of identicalness and provide performance measures for the Federated Averaging algorithm. We show that performance degrades as distributions differ more, and propose a mitigation strategy via server momentum. Experiments on CIFAR-10 demonstrate improved classification performance over a range of non-identicalness, with classification accuracy improved from 30.1% to 76.9% in the most skewed settings.

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 "salvaging federated learning by local adaptation"):
  with members:
    - "differential privacy has disparate impact on model accuracy"
    - "advances and open problems in federated learning"
    - "one shot federated learning"
    - "improving federated learning personalization via model agnostic meta learning"
    - "towards federated learning at scale system design"
    - "detox a redundancy based framework for faster and more robust gradient aggregation"
    - "federated evaluation of on device personalization"
    - "adversarial initialization when your network performs the way i want"

Connected through hyperedge HE-3 (induced by source paper "federated visual classification with real world data distribution"):
  with members:
    - "scaffold stochastic controlled averaging for on device federated learning"
    - "large scale landmark retrieval recognition under a noisy and diverse dataset"
    - "advances and open problems in federated learning"
    - "towards federated learning at scale system design"
    - "federated learning challenges methods and future directions"
    - "the non iid data quagmire of decentralized machine learning"
    - "real world image datasets for federated learning"
    - "mobilenetv2 inverted residuals and linear bottlenecks"

Connected through hyperedge HE-4 (induced by source paper "adaptive federated optimization"):
  with members:
    - "scaffold stochastic controlled averaging for on device federated learning"
    - "advances and open problems in federated learning"
    - "towards federated learning at scale system design"
    - "the error feedback framework better rates for sgd with delayed gradients and compressed communication"
    - "global convergence of adaptive gradient methods for an over parameterized neural network"
    - "federated learning challenges methods and future directions"
    - "on the convergence of adam and beyond"
    - "the non iid data quagmire of decentralized machine 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)