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: "federated meta learning with fast convergence and efficient communication"
  Abstract: Statistical and systematic challenges in collaboratively training machine learning models across distributed networks of mobile devices have been the bottlenecks in the real-world application of federated learning. In this work, we show that meta-learning is a natural choice to handle these issues, and propose a federated meta-learning framework FedMeta, where a parameterized algorithm (or meta-learner) is shared, instead of a global model in previous approaches. We conduct an extensive empirical evaluation on LEAF datasets and a real-world production dataset, and demonstrate that FedMeta achieves a reduction in required communication cost by 2.82-4.33 times with faster convergence, and an increase in accuracy by 3.23%-14.84% as compared to Federated Averaging (FedAvg) which is a leading optimization algorithm in federated learning. Moreover, FedMeta preserves user privacy since only the parameterized algorithm is transmitted between mobile devices and central servers, and no raw data is collected onto the servers.

Connected through hyperedge HE-1 (induced by source paper "survey of personalization techniques for federated learning"):
  with members:
    - "three approaches for personalization with applications to federated learning"
    - "personalized federated learning for intelligent iot applications a cloud edge based framework"
    - "data selection for federated learning with relevant and irrelevant data at clients"
    - "federated learning of a mixture of global and local models"
    - "salvaging federated learning by local adaptation"
    - "personalized federated learning a meta learning approach"
    - "federated learning with personalization layers"
    - "adaptive gradient based meta learning methods"

Connected through hyperedge HE-2 (induced by source paper "metaselector meta learning for recommendation with user level adaptive model selection"):
  with members:
    - "a closer look at few shot classification"
    - "neural graph collaborative filtering"
    - "meta learning pac bayes priors in model averaging"
    - "on first order meta learning algorithms"
    - "reptile a scalable metalearning algorithm"
    - "one at a time a meta learning recommender system for recommendation algorithm selection on micro level"
    - "neural factorization machines for sparse predictive analytics"
    - "field aware factorization machines in a real world online advertising system"

Connected through hyperedge HE-3 (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"
    - "one shot federated learning"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "dynamic federated learning"):
  with members:
    - "adaptive gradient based meta learning methods"
    - "variational federated multi task learning"
    - "asynchronous federated optimization"
    - "agnostic federated learning"
    - "client edge cloud hierarchical federated learning"
    - "first analysis of local gd on heterogeneous data"
    - "federated learning strategies for improving communication efficiency"
    - "federated optimization for heterogeneous networks"

Connected through hyperedge HE-5 (induced by source paper "federated machine learning concept and applications"):
  with members:
    - "on device federated learning via blockchain and its latency analysis"
    - "exploiting unintended feature leakage in collaborative learning"
    - "federated learning strategies for improving communication efficiency"
    - "securing distributed machine learning in high dimensions"
    - "entity resolution and federated learning get a federated resolution"
    - "how to backdoor federated learning"
    - "chameleon a hybrid secure computation framework for machine learning applications"
    - "federated meta learning for recommendation"

Connected through hyperedge HE-6 (induced by source paper "learning to recommend via meta parameter partition"):
  with members:
    - "learn to grow a continual structure learning framework for overcoming catastrophic forgetting"
    - "online meta learning"
    - "on first order meta learning algorithms"
    - "deep reinforcement learning for page wise recommendations"
    - "generative adversarial user model for reinforcement learning based recommendation system"
    - "federated meta learning for recommendation"
    - "deep reinforcement learning for list wise recommendations"
    - "memory aware synapses learning what not to forget"

Connected through hyperedge HE-7 (induced by source paper "fedhealth a federated transfer learning framework for wearable healthcare"):
  with members:
    - "beyond sharing weights for deep domain adaptation"
    - "federated machine learning concept and applications"
    - "easy transfer learning by exploiting intra domain structures"
    - "secureboost a lossless federated learning framework"
    - "deep learning for sensor based activity recognition a survey"
    - "visual domain adaptation with manifold embedded distribution alignment"
    - "federated meta learning for recommendation"
    - "balanced distribution adaptation for transfer learning"

Connected through hyperedge HE-8 (induced by source paper "federated learning with additional mechanisms on clients to reduce communication costs"):
  with members:
    - "federated learning strategies for improving communication efficiency"
    - "leaf a benchmark for federated settings"
    - "communication efficient on device machine learning federated distillation and augmentation under non iid private data"
    - "federated meta learning for recommendation"
    - "federated learning with non iid data"
    - "expanding the reach of federated learning by reducing client resource requirements"
    - "continual learning through synaptic intelligence"
    - "federated multi task 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)