human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "what you expect is not what you get questioning reconstruction classification correlation of stacked convolutional auto encoder features"
  Abstract: In this paper, we thoroughly investigate the quality of features produced by deep neural network architectures obtained by stacking and convolving Auto-Encoders. In particular, we are interested into the relation of their reconstruction score with their performance on document layout analysis. When using Auto-Encoders, intuitively one could assume that features which are good for reconstruction will also lead to high classification accuracies. However, we prove that this is not always the case. We examine the reconstruction score, training error and the results obtained if we were to use the same features for both input reconstruction and a classification task. We show that the reconstruction score is not a good metric because it is biased by the decoder quality. Furthermore, experimental results suggest that there is no correlation between the reconstruction score and the quality of features for a classification task and that given the network size and configuration it is not possible to make assumptions on its training error magnitude. Therefore we conclude that both, reconstruction score and training error should not be used jointly to evaluate the quality of the features produced by a Stacked Convolutional Auto-Encoders for a classification task. Consequently one should independently investigate the network classification abilities directly.

Connected through hyperedge HE-1 (induced by source paper "historical document image segmentation with lda initialized deep neural networks"):
  with members:
    - "open evaluation tool for layout analysis of document images"
    - "pca initialized deep neural networks applied to document image analysis"
    - "deep learning in neural networks"
    - "deep linear discriminant analysis"
    - "data dependent initializations of convolutional neural networks"

Connected through hyperedge HE-2 (induced by source paper "open evaluation tool for layout analysis of document images"):
  with members:
    - "machine learning in automated text categorization"

Question: Please tell me which class the center node 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.CV(Computer Vision and Pattern Recognition)