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 "deep stacked stochastic configuration networks for lifelong learning of non stationary data streams"):
  Abstract: The concept of SCN offers a fast framework with universal approximation guarantee for lifelong learning of non-stationary data streams. Its adaptive scope selection property enables for proper random generation of hidden unit parameters advancing conventional randomized approaches constrained with a fixed scope of random parameters. This paper proposes deep stacked stochastic configuration network (DSSCN) for continual learning of non-stationary data streams which contributes two major aspects: 1) DSSCN features a self-constructing methodology of deep stacked network structure where hidden unit and hidden layer are extracted automatically from continuously generated data streams; 2) the concept of SCN is developed to randomly assign inverse covariance matrix of multivariate Gaussian function in the hidden node addition step bypassing its computationally prohibitive tuning phase. Numerical evaluation and comparison with prominent data stream algorithms under two procedures: periodic hold-out and prequential test-then-train processes demonstrate the advantage of proposed methodology.

Includes members:
  Member "autonomous deep learning incremental learning of denoising autoencoder for evolving data streams":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "autonomous deep learning continual learning approach for dynamic environments")
  Member "evolving ensemble fuzzy classifier":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "online tool condition monitoring based on parsimonious ensemble")
  Member "progressive neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "review analyze and design a comprehensive deep reinforcement learning framework")
  Member "lifelong learning with dynamically expandable networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "online fast adaptation and knowledge accumulation a new approach to continual learning")
  Member "stochastic configuration networks fundamentals and algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep randomized neural networks")
  Member "online deep learning learning deep neural networks on the fly":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "pa cache learning based popularity aware content caching in edge networks")
  Member "stochastic configuration networks ensemble with heterogeneous features for large scale data analytics":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "fast construction of correcting ensembles for legacy artificial intelligence systems algorithms and a case study")
  Member "deep stochastic configuration networks with universal approximation property":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "deep randomized neural networks")

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.LG(Machine Learning)