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 "towards fair and decentralized privacy preserving deep learning"):
  Abstract: In current deep learning paradigms, the standalone framework tends to result in overfitting and low utility. This problem can be addressed by either a centralized framework that deploys a central server to train a global model on the joint data from all parties, or a distributed framework that leverages a parameter server to aggregate local model updates. Server-based frameworks unfortunately suffer from the single-point-of-failure problem, and the decentralized framework is born to be resistant to it. However, all the existing collaborative learning frameworks (distributed or decentralized) have overlooked an important aspect of participation: fairness. In particular, all parties can get similar models, even the ones merely making marginal contribution with low-quality data. To address this issue, we propose a decentralized privacy-preserving deep learning framework called DPPDL. It makes the first-ever investigation on the collaborative fairness in deep learning, and proposes two novel strategies to guarantee both fairness and privacy. We experimentally demonstrate that, on benchmark image datasets, fairness, privacy and accuracy in collaborative deep learning can now be effectively achieved at the same time by our proposed DPPDL. Moreover, it provides a viable solution to detect and reduce the impact of low-quality parties in the collaborative learning system.

Includes members:
  Member "modelchain decentralized privacy preserving healthcare predictive modeling framework on private blockchain networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "towards fair and decentralized privacy preserving deep learning with blockchain")
  Member "dimensionality driven learning with noisy labels":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "dividemix learning with noisy labels as semi supervised learning")
  Member "iterative learning with open set noisy labels":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "dividemix learning with noisy labels as semi supervised learning")
  Member "differentially private fair learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "removing disparate impact of differentially private stochastic gradient descent on model accuracy")
  Member "learning differentially private recurrent language models":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "do we really need to access the source data source hypothesis transfer for unsupervised domain adaptation")
  Member "deep models under the gan information leakage from collaborative deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "trends and advancements in deep neural network communication")
  Member "federated learning of deep networks using model averaging":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "neural data server a large scale search engine for transfer learning data")
  Member "crypto nets neural networks over encrypted data":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "privacy preserving deep visual recognition an adversarial learning framework and a new dataset")

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)