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: "multi objective evolutionary federated learning"
  Abstract: Federated learning is an emerging technique used to prevent the leakage of private information. Unlike centralized learning that needs to collect data from users and store them collectively on a cloud server, federated learning makes it possible to learn a global model while the data are distributed on the users' devices. However, compared with the traditional centralized approach, the federated setting consumes considerable communication resources of the clients, which is indispensable for updating global models and prevents this technique from being widely used. In this paper, we aim to optimize the structure of the neural network models in federated learning using a multi-objective evolutionary algorithm to simultaneously minimize the communication costs and the global model test errors. A scalable method for encoding network connectivity is adapted to federated learning to enhance the efficiency in evolving deep neural networks. Experimental results on both multilayer perceptrons and convolutional neural networks indicate that the proposed optimization method is able to find optimized neural network models that can not only significantly reduce communication costs but also improve the learning performance of federated learning compared with the standard fully connected neural networks.

Connected through hyperedge HE-1 (induced by source paper "real time federated evolutionary neural architecture search"):
  with members:
    - "federated collaborative filtering for privacy preserving personalized recommendation system"
    - "searching for a robust neural architecture in four gpu hours"
    - "communication efficient federated deep learning with asynchronous model update and temporally weighted aggregation"
    - "single path one shot neural architecture search with uniform sampling"
    - "fairnas rethinking evaluation fairness of weight sharing neural architecture search"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "slimmable neural networks"
    - "federated learning strategies for improving communication efficiency"

Connected through hyperedge HE-2 (induced by source paper "ternary compression for communication efficient federated learning"):
  with members:
    - "a survey on federated learning systems vision hype and reality for data privacy and protection"
    - "towards federated learning at scale system design"
    - "robust and communication efficient federated learning from non iid data"
    - "federated machine learning concept and applications"
    - "federated learning in mobile edge networks a comprehensive survey"
    - "model compression via distillation and quantization"
    - "adaptive federated learning in resource constrained edge computing systems"
    - "federated learning with non iid data"

Connected through hyperedge HE-3 (induced by source paper "practical and bilateral privacy preserving federated learning"):
  with members:
    - "skin lesion analysis toward melanoma detection 2018 a challenge hosted by the international skin imaging collaboration isic"
    - "advances and open problems in federated learning"
    - "privacy preserving federated brain tumour segmentation"
    - "communication efficient federated deep learning with asynchronous model update and temporally weighted aggregation"
    - "semi cyclic stochastic gradient descent"
    - "federated learning for healthcare informatics"
    - "federated learning challenges methods and future directions"
    - "agnostic 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 "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-6 (induced by source paper "on improving deep learning generalization with adaptive sparse connectivity"):
  with members:
    - "sparse evolutionary deep learning with over one million artificial neurons on commodity hardware"
    - "low memory neural network training a technical report"
    - "scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science"
    - "snip single shot network pruning based on connection sensitivity"
    - "exploring generalization in deep learning"
    - "computing nonvacuous generalization bounds for deep stochastic neural networks with many more parameters than training data"
    - "a pac bayesian approach to spectrally normalized margin bounds for neural networks"
    - "exploring sparsity in recurrent neural networks"

Connected through hyperedge HE-7 (induced by source paper "communication efficient federated deep learning with asynchronous model update and temporally weighted aggregation"):
  with members:
    - "federated learning strategies for improving communication efficiency"
    - "lstm a search space odyssey"
    - "differentially private federated learning a client level perspective"
    - "deep convolutional neural networks for computer aided detection cnn architectures dataset characteristics and transfer learning"
    - "communication efficient learning of deep networks from decentralized data"
    - "federated optimization distributed machine learning for on device intelligence"
    - "federated optimization distributed optimization beyond the datacenter"
    - "distributed optimization with arbitrary local solvers"

Connected through hyperedge HE-8 (induced by source paper "evolving and understanding sparse deep neural networks using cosine similarity"):
  with members:
    - "parameter efficient training of deep convolutional neural networks by dynamic sparse reparameterization"
    - "scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science"
    - "accelerating training of deep neural networks via sparse edge processing"
    - "in datacenter performance analysis of a tensor processing unit"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "training sparse neural networks"
    - "a topological insight into restricted boltzmann machines"
    - "learning both weights and connections for efficient neural networks"

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)