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 "big data intelligence using distributed deep neural networks"):
  Abstract: Large amount of data is often required to train and deploy useful machine learning models in industry. Smaller enterprises do not have the luxury of accessing enough data for machine learning, For privacy sensitive fields such as banking, insurance and healthcare, aggregating data to a data warehouse poses a challenge of data security and limited computational resources. These challenges are critical when developing machine learning algorithms in industry. Several attempts have been made to address the above challenges by using distributed learning techniques such as federated learning over disparate data stores in order to circumvent the need for centralised data aggregation. This paper proposes an improved algorithm to securely train deep neural networks over several data sources in a distributed way, in order to eliminate the need to centrally aggregate the data and the need to share the data thus preserving privacy. The proposed method allows training of deep neural networks using data from multiple de-linked nodes in a distributed environment and to secure the representation shared during training. Only a representation of the trained models (network architecture and weights) are shared. The algorithm was evaluated on existing healthcare patients data and the performance of this implementation was compared to that of a regular deep neural network trained on a single centralised architecture. This algorithm will pave a way for distributed training of neural networks on privacy sensitive applications where raw data may not be shared directly or centrally aggregating this data in a data warehouse is not feasible.

Includes members:
  Member "distributed learning of deep neural network over multiple agents":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "industrial scale privacy preserving deep neural network")
  Member "emnist an extension of mnist to handwritten letters":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "anomalous instance detection in deep learning a survey")
  Member "communication efficient learning of deep networks from decentralized data":
    also connected through other hyperedges:
      - Hyperedge HE-3 (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.DC(Distributed, Parallel, and Cluster Computing)