human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "a survey towards federated semi supervised learning"):
  Abstract: The success of Artificial Intelligence (AI) should be largely attributed to the accessibility of abundant data. However, this is not exactly the case in reality, where it is common for developers in industry to face insufficient, incomplete and isolated data. Consequently, federated learning was proposed to alleviate such challenges by allowing multiple parties to collaboratively build machine learning models without explicitly sharing their data and in the meantime, preserve data privacy. However, existing algorithms of federated learning mainly focus on examples where, either the data do not require explicit labeling, or all data are labeled. Yet in reality, we are often confronted with the case that labeling data itself is costly and there is no sufficient supply of labeled data. While such issues are commonly solved by semi-supervised learning, to the best of knowledge, no existing effort has been put to federated semi-supervised learning. In this survey, we briefly summarize prevalent semi-supervised algorithms and make a brief prospect into federated semi-supervised learning, including possible methodologies, settings and challenges.

Includes members:
  Member "federated learning with matched averaging":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "federated continual learning with adaptive parameter communication")
  Member "advances and open problems in federated learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "trends and advancements in deep neural network communication")
  Member "adaptive image feature learning for disease classification using inductive graph networks":
    also connected through other hyperedges:
      (none)
  Member "unsupervised data augmentation for consistency training":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "affinity and diversity quantifying mechanisms of data augmentation")
  Member "federated machine learning concept and applications":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "the future of digital health with federated learning")
  Member "mixmatch a holistic approach to semi supervised learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "domain adaptive ensemble learning")
  Member "federated adversarial domain adaptation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "do we really need to access the source data source hypothesis transfer for unsupervised domain adaptation")
  Member "secureboost a lossless federated learning framework":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "faster on device training using new federated momentum algorithm")

Question: Please tell me which class the source paper 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)