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: "a survey on federated learning systems vision hype and reality for data privacy and protection"
  Abstract: Federated learning has been a hot research area in enabling the collaborative training of machine learning models among different organizations under the privacy restrictions. As researchers try to support more machine learning models with different privacy-preserving approaches, there is a requirement in developing systems and infrastructures to ease the development of various federated learning algorithms. Just like deep learning systems such as Caffe, PyTorch, and Tensorflow that boost the development of deep learning algorithms, federated learning systems are equivalently important, and face challenges from various issues such as unpractical system assumptions, scalability and efficiency. Inspired by federated systems in other fields such as databases and cloud computing, we investigate the existing characteristics of federated learning systems. We find that two important features for federated systems in other fields, i.e., heterogeneity and autonomy, are rarely considered in the existing federated learning systems. Moreover, we provide a thorough categorization for federated learning systems according to six different aspects, including data partition, machine learning model, privacy mechanism, communication architecture, scale of federation and motivation of federation. The categorization can help the design of federated learning systems as shown in our case studies. Lastly, we take a systematic comparison among the existing federated learning systems and present future research opportunities and directions.

Connected through hyperedge HE-1 (induced by source paper "ternary compression for communication efficient federated learning"):
  with members:
    - "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"
    - "multi objective evolutionary federated learning"
    - "federated learning with non iid data"

Connected through hyperedge HE-2 (induced by source paper "learning to detect malicious clients for robust federated learning"):
  with members:
    - "incentive design for efficient federated learning in mobile networks a contract theory approach"
    - "can you really backdoor federated learning"
    - "semi cyclic stochastic gradient descent"
    - "local model poisoning attacks to byzantine robust federated learning"
    - "federated learning challenges methods and future directions"
    - "federated variance reduced stochastic gradient descent with robustness to byzantine attacks"
    - "federated machine learning concept and applications"
    - "fedmd heterogenous federated learning via model distillation"

Connected through hyperedge HE-3 (induced by source paper "practical federated gradient boosting decision trees"):
  with members:
    - "agnostic federated learning"
    - "federated machine learning concept and applications"
    - "secureboost a lossless federated learning framework"
    - "boosting privately privacy preserving federated extreme boosting for mobile crowdsensing"
    - "secure federated transfer learning"
    - "federated multi task learning"
    - "xgboost a scalable tree boosting system"
    - "federated learning of deep networks using model averaging"

Connected through hyperedge HE-4 (induced by source paper "privacy preserving gradient boosting decision trees"):
  with members:
    - "practical federated gradient boosting decision trees"
    - "multi layered gradient boosting decision trees"
    - "towards demystifying membership inference attacks"
    - "catboost unbiased boosting with categorical features"
    - "differentially private mixture of generative neural networks"
    - "xgboost a scalable tree boosting system"
    - "membership inference attacks against machine learning models"
    - "scikit learn machine learning in python"

Connected through hyperedge HE-5 (induced by source paper "learn electronic health records by fully decentralized federated learning"):
  with members:
    - "federated machine learning concept and applications"
    - "communication efficient learning of deep networks from decentralized data"
    - "next in network nonconvex optimization"

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)