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 spark ml driven preprocessing approach for deep learning based scholarly data applications"):
  Abstract: Big data has found applications in multiple domains. One of the largest sources of textual big data is scientific documents and papers. Big scholarly data have been used in numerous ways to create innovative applications such as collaborator discovery, expert finding and research management systems. With the advent of advanced machine and deep learning techniques, the accuracy and novelty of such applications have risen manifold. However, the biggest challenge in the development of deep learning models for scholarly applications in cloud based environment is the underutilization of resources because of the excessive time taken by textual preprocessing. This paper presents a preprocessing pipeline that makes use of Spark for data ingestion and Spark ML for pipelining preprocessing tasks. The evaluation of the proposed work is done using a case study, which uses LSTM based text summarization for generating title or summary from abstract of any research. The ingestion, preprocessing and cumulative time for the proposed approach (P3SAPP) is much lower than the conventional approach (CA), which manifests in reduction of costs as well.

Includes members:
  Member "scake semantic connectivity aware keyword extraction":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "complex network based supervised keyword extractor")
  Member "pabed a tool for big education data analysis":
    also connected through other hyperedges:
      (none)
  Member "deep keyphrase generation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "two huge title and keyword generation corpora of research articles")
  Member "accurate large minibatch sgd training imagenet in 1 hour":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning representations by predicting bags of visual words")
  Member "modelling sentence pairs with tree structured attentive encoder":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "improving tree lstm with tree attention")
  Member "recurrent neural network for text classification with multi task learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "meta embeddings based on self attention")
  Member "sequence to sequence rnns for text summarization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "prophetnet predicting future n gram for sequence to sequence pre training")
  Member "mllib machine learning in apache spark":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "multi layer optimizations for end to end data analytics")

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)