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 efficient on device machine learning federated distillation and augmentation under non iid private data"
  Abstract: On-device machine learning (ML) enables the training process to exploit a massive amount of user-generated private data samples. To enjoy this benefit, inter-device communication overhead should be minimized. With this end, we propose federated distillation (FD), a distributed model training algorithm whose communication payload size is much smaller than a benchmark scheme, federated learning (FL), particularly when the model size is large. Moreover, user-generated data samples are likely to become non-IID across devices, which commonly degrades the performance compared to the case with an IID dataset. To cope with this, we propose federated augmentation (FAug), where each device collectively trains a generative model, and thereby augments its local data towards yielding an IID dataset. Empirical studies demonstrate that FD with FAug yields around 26x less communication overhead while achieving 95-98% test accuracy compared to FL.

Connected through hyperedge HE-1 (induced by source paper "faster on device training using new federated momentum algorithm"):
  with members:
    - "advances and open problems in federated learning"
    - "towards federated learning at scale system design"
    - "federated machine learning concept and applications"
    - "secureboost a lossless federated learning framework"
    - "federated learning strategies for improving communication efficiency"
    - "federated optimization for heterogeneous networks"
    - "leaf a benchmark for federated settings"
    - "federated learning for mobile keyboard prediction"

Connected through hyperedge HE-2 (induced by source paper "personalized federated learning for intelligent iot applications a cloud edge based framework"):
  with members:
    - "hfel joint edge association and resource allocation for cost efficient hierarchical federated edge learning"
    - "fedhealth a federated transfer learning framework for wearable healthcare"
    - "federated learning with personalization layers"
    - "edge intelligence paving the last mile of artificial intelligence with edge computing"
    - "improving federated learning personalization via model agnostic meta learning"
    - "federated learning for healthcare informatics"
    - "wireless federated distillation for distributed edge learning with heterogeneous data"
    - "federated learning challenges methods and future directions"

Connected through hyperedge HE-3 (induced by source paper "decentralized learning of generative adversarial networks from non iid data"):
  with members:
    - "towards federated learning at scale system design"
    - "cgans with projection discriminator"
    - "cinic 10 is not imagenet or cifar 10"
    - "demystifying parallel and distributed deep learning an in depth concurrency analysis"
    - "federated learning strategies for improving communication efficiency"
    - "md gan multi discriminator generative adversarial networks for distributed datasets"
    - "spectral normalization for generative adversarial networks"
    - "how to backdoor federated learning"

Connected through hyperedge HE-4 (induced by source paper "performance analysis and characterization of training deep learning models on mobile devices"):
  with members:
    - "towards federated learning at scale system design"
    - "agnostic federated learning"
    - "applied federated learning improving google keyboard query suggestions"
    - "deepcache principled cache for mobile deep vision"
    - "federated learning strategies for improving communication efficiency"
    - "runtime concurrency control and operation scheduling for high performance neural network training"
    - "beyond data and model parallelism for deep neural networks"
    - "latency and throughput characterization of convolutional neural networks for mobile computer vision"

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"
    - "memory replay gans learning to generate images from new categories without forgetting"

Connected through hyperedge HE-6 (induced by source paper "performance analysis and characterization of training deep learning models on nvidia tx2"):
  with members:
    - "towards federated learning at scale system design"
    - "agnostic federated learning"
    - "applied federated learning improving google keyboard query suggestions"
    - "deepcache principled cache for mobile deep vision"
    - "federated learning strategies for improving communication efficiency"
    - "runtime concurrency control and operation scheduling for high performance neural network training"
    - "beyond data and model parallelism for deep neural networks"
    - "federated optimization for heterogeneous networks"

Connected through hyperedge HE-7 (induced by source paper "q gadmm quantized group admm for communication efficient decentralized machine learning"):
  with members:
    - "extreme urllc vision challenges and key enablers"
    - "communication efficient massive uav online path control federated learning meets mean field game theory"
    - "predictive control and communication co design a gaussian process regression approach"
    - "decentralized stochastic optimization and gossip algorithms with compressed communication"
    - "massive autonomous uav path planning a neural network based mean field game theoretic approach"
    - "accelerating federated learning via momentum gradient descent"
    - "advances and open problems in federated learning"
    - "gadmm fast and communication efficient framework for distributed machine learning"

Connected through hyperedge HE-8 (induced by source paper "the disruptions of 5g on data driven technologies and applications"):
  with members:
    - "an e2e simulator for 5g nr networks"
    - "federated machine learning concept and applications"
    - "blockchain goes green an analysis of blockchain on low power nodes"
    - "recommendations with negative feedback via pairwise deep reinforcement learning"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "regularized evolution for image classifier architecture search"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "proxylessnas direct neural architecture search on target task and hardware"

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)