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 "privacy preserving weighted federated learning within oracle aided mpc framework"):
  Abstract: This paper studies privacy-preserving weighted federated learning within the oracle-aided multi-party computation (MPC) framework. The contribution of this paper mainly comprises the following three-fold: #R##N#In the first fold, a new notion which we call weighted federated learning (wFL) is introduced and formalized inspired by McMahan et al.'s seminal paper. The weighted federated learning concept formalized in this paper differs from that presented in McMahan et al.'s paper since both addition and multiplication operations are executed over ciphers in our model while these operations are executed over plaintexts in McMahan et al.'s model. #R##N#In the second fold, an oracle-aided MPC solution for computing weighted federated learning is formalized by decoupling the security of federated learning systems from that of underlying multi-party computations. Our decoupling formulation may benefit machine learning developers to select their best security practices from the state-of-the-art security tool sets; #R##N#In the third fold, a concrete solution to the weighted federated learning problem is presented and analysed. The security of our implementation is guaranteed by the security composition theorem assuming that the underlying multiplication algorithm is secure against honest-but-curious adversaries.

Includes members:
  Member "extending relational query processing with ml inference":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "cloudy with high chance of dbms a 10 year prediction for enterprise grade ml")
  Member "mlsys the new frontier of machine learning systems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "distributed learning of deep neural networks using independent subnet training")
  Member "towards federated learning at scale system design":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "do we really need to access the source data source hypothesis transfer for unsupervised domain adaptation")
  Member "cloudy with high chance of dbms a 10 year prediction for enterprise grade ml":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "vamsa tracking provenance in data science scripts")
  Member "federated machine learning concept and applications":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "the future of digital health with federated learning")
  Member "entity resolution and federated learning get a federated resolution":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "multi participant multi class vertical federated learning")
  Member "private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "multi participant multi class vertical federated learning")
  Member "communication efficient learning of deep networks from decentralized data":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "secure and robust machine learning for healthcare a survey")

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.CR(Cryptography and Security)