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 "mechanism design for multi party machine learning"):
  Abstract: In a multi-party machine learning system, different parties cooperate on optimizing towards better models by sharing data in a privacy-preserving way. A major challenge in the learning is the incentive issue. For example, if there is competition among the parties, one may strategically hide his data to prevent other parties from getting better models. #R##N#In this paper, we study the problem through the lens of mechanism design. Compared with the standard mechanism design setting, our setting has several fundamental differences. First, each agent's valuation has externalities that depend on others' true types. We call this setting \emph{mechanism design with type-imposed externalities}. Second, each agent can only misreport a lower type, but not the other way round. We show that some results (e.g., the truthfulness of the VCG mechanism) in the standard mechanism design setting fail to hold. #R##N#We provide the optimal truthful mechanism in the quasi-monotone utility setting. We also provide necessary and sufficient conditions for truthful mechanisms in the most general case. Finally, we show the existence of such mechanisms are highly affected by the market growth rate and give empirical analysis.

Includes members:
  Member "incentive design for efficient federated learning in mobile networks a contract theory approach":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning to detect malicious clients for robust federated learning")
  Member "federated learning strategies for improving communication efficiency":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "trends and advancements in deep neural network communication")
  Member "strategyproof linear regression in high dimensions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "grinding the space learning to classify against strategic agents")
  Member "privacy preserving visual learning using doubly permuted homomorphic encryption":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep poisoning functions towards robust privacy safe image data sharing")
  Member "communication efficient learning of deep networks from decentralized data":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "secure and robust machine learning for healthcare a survey")
  Member "truthful linear regression":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "causal feature discovery through strategic modification")
  Member "auctions with severely bounded communication":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "communication distortion and randomness in metric voting")

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.MA(Multiagent Systems)