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: "one shot federated learning"
  Abstract: We present one-shot federated learning, where a central server learns a global model over a network of federated devices in a single round of communication. Our approach - drawing on ensemble learning and knowledge aggregation - achieves an average relative gain of 51.5% in AUC over local baselines and comes within 90.1% of the (unattainable) global ideal. We discuss these methods and identify several promising directions of future work.

Connected through hyperedge HE-1 (induced by source paper "think locally act globally federated learning with local and global representations"):
  with members:
    - "towards federated learning at scale system design"
    - "quantized epoch sgd for communication efficient distributed learning"
    - "improved adversarial learning for fair classification"
    - "agnostic federated learning"
    - "learning fair representations via an adversarial framework"
    - "gender bias in contextualized word embeddings"
    - "federated machine learning concept and applications"
    - "demystifying parallel and distributed deep learning an in depth concurrency analysis"

Connected through hyperedge HE-2 (induced by source paper "federated learning for resource constrained iot devices panoramas and state of the art"):
  with members:
    - "elfish resource aware federated learning on heterogeneous edge devices"
    - "model pruning enables efficient federated learning on edge devices"
    - "privacy is what we care about experimental investigation of federated learning on edge devices"
    - "towards federated learning at scale system design"
    - "compressing rnns for iot devices by 15 38x using kronecker products"
    - "distilling on device intelligence at the network edge"
    - "federated learning challenges methods and future directions"
    - "federated machine learning concept and applications"

Connected through hyperedge HE-3 (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"
    - "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"
    - "measuring the effects of non identical data distribution for federated visual classification"
    - "adversarial initialization when your network performs the way i want"

Connected through hyperedge HE-4 (induced by source paper "personalized federated learning a meta learning approach"):
  with members:
    - "scaffold stochastic controlled averaging for on device federated learning"
    - "adaptive gradient based meta learning methods"
    - "tighter theory for local sgd on identical and heterogeneous data"
    - "improving federated learning personalization via model agnostic meta learning"
    - "on the convergence of local descent methods in federated learning"
    - "alpha maml adaptive model agnostic meta learning"
    - "fedpaq a communication efficient federated learning method with periodic averaging and quantization"
    - "hyper sphere quantization communication efficient sgd for federated learning"

Connected through hyperedge HE-5 (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"
    - "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"
    - "an exact quantized decentralized gradient descent algorithm"

Connected through hyperedge HE-6 (induced by source paper "a federated learning approach for mobile packet classification"):
  with members:
    - "towards federated learning at scale system design"
    - "client selection for federated learning with heterogeneous resources in mobile edge"
    - "federated heavy hitters discovery with differential privacy"
    - "analyzing federated learning through an adversarial lens"
    - "exploiting unintended feature leakage in collaborative learning"
    - "federated learning strategies for improving communication efficiency"
    - "beyond inferring class representatives user level privacy leakage from federated learning"
    - "a hybrid approach to privacy preserving federated learning"

Connected through hyperedge HE-7 (induced by source paper "deep gamblers learning to abstain with portfolio theory"):
  with members:
    - "selectivenet a deep neural network with an integrated reject option"
    - "on the convergence of adam and beyond"
    - "multimodal language analysis with recurrent multistage fusion"
    - "deep learning is robust to massive label noise"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "neural machine translation and sequence to sequence models a tutorial"
    - "improving the adversarial robustness and interpretability of deep neural networks by regularizing their input gradients"

Connected through hyperedge HE-8 (induced by source paper "on the convergence of local descent methods in federated learning"):
  with members:
    - "scaffold stochastic controlled averaging for on device federated learning"
    - "federated learning challenges methods and future directions"
    - "agnostic federated learning"
    - "first analysis of local gd on heterogeneous data"
    - "local sgd with periodic averaging tighter analysis and adaptive synchronization"
    - "fair resource allocation in federated learning"
    - "efficient decentralized deep learning by dynamic model averaging"
    - "analyzing federated learning through an adversarial lens"

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)