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 "learning discriminative features using encoder decoder type deep neural nets"):
  Abstract: As machine learning is applied to an increasing variety of complex problems, which are defined by high dimensional and complex data sets, the necessity for task oriented feature learning grows in importance. With the advancement of Deep Learning algorithms, various successful feature learning techniques have evolved. In this paper, we present a novel way of learning discriminative features by training Deep Neural Nets which have Encoder or Decoder type architecture similar to an Autoencoder. We demonstrate that our approach can learn discriminative features which can perform better at pattern classification tasks when the number of training samples is relatively small in size.

Includes members:
  Member "kernel principal component analysis and its applications in face recognition and active shape models":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "empirical evaluation of kernel pca approximation methods in classification tasks")
  Member "deep lambertian networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "temporal transformer networks joint learning of invariant and discriminative time warping")
  Member "deep big simple neural nets for handwritten digit recognition":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "training simplification and model simplification for deep learning a minimal effort back propagation method")
  Member "a mirroring theorem and its application to a new method of unsupervised hierarchical pattern classification":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "some theorems for feed forward neural networks")
  Member "pattern recognition and memory mapping using mirroring neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "some theorems for feed forward 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)