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 "two dimensional stochastic configuration networks for image data analytics"):
  Abstract: Stochastic configuration networks (SCNs) as a class of randomized learner model have been successfully employed in data analytics due to its universal approximation capability and fast modelling property. The technical essence lies in stochastically configuring hidden nodes (or basis functions) based on a supervisory mechanism rather than data-independent randomization as usually adopted for building randomized neural networks. Given image data modelling tasks, the use of one-dimensional SCNs potentially demolishes the spatial information of images, and may result in undesirable performance. This paper extends the original SCNs to two-dimensional version, termed 2DSCNs, for fast building randomized learners with matrix-inputs. Some theoretical analyses on the goodness of 2DSCNs against SCNs, including the complexity of the random parameter space, and the superiority of generalization, are presented. Empirical results over one regression, four benchmark handwritten digits classification, and two human face recognition datasets demonstrate that the proposed 2DSCNs perform favourably and show good potential for image data analytics.

Includes members:
  Member "learning less overlapping representations":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "enhancing decision tree based interpretation of deep neural networks through l1 orthogonal regularization")
  Member "recovery guarantees for one hidden layer neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "backward feature correction how deep learning performs deep learning")
  Member "stochastic configuration networks ensemble with heterogeneous features for large scale data analytics":
    also connected through other hyperedges:
      - Hyperedge HE-3 (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-4 (induced by source paper "deep randomized neural networks")
  Member "matrix neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "temporal attention augmented bilinear network for financial time series data analysis")
  Member "approximation with random bases":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep randomized neural networks")
  Member "matrix variate rbm model with gaussian distributions":
    also connected through other hyperedges:
      (none)
  Member "on the generalization error bounds of neural networks under diversity inducing mutual angular regularization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "on correlation of features extracted by deep 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)