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: "autoencoder inspired unsupervised feature selection"
  Abstract: High-dimensional data in many areas such as computer vision and machine learning tasks brings in computational and analytical difficulty. Feature selection which selects a subset from observed features is a widely used approach for improving performance and effectiveness of machine learning models with high-dimensional data. In this paper, we propose a novel AutoEncoder Feature Selector (AEFS) for unsupervised feature selection which combines autoencoder regression and group lasso tasks. Compared to traditional feature selection methods, AEFS can select the most important features by excavating both linear and nonlinear information among features, which is more flexible than the conventional self-representation method for unsupervised feature selection with only linear assumptions. Experimental results on benchmark dataset show that the proposed method is superior to the state-of-the-art method.

Connected through hyperedge HE-1 (induced by source paper "attribute aware attention model for fine grained representation learning"):
  with members:
    - "improving person re identification by attribute and identity learning"
    - "object part attention model for fine grained image classification"
    - "person re identification by camera correlation aware feature augmentation"
    - "scalable person re identification on supervised smoothed manifold"
    - "a discriminatively learned cnn embedding for person reidentification"
    - "re ranking person re identification with k reciprocal encoding"
    - "person re identification by deep joint learning of multi loss classification"
    - "end to end comparative attention networks for person re identification"

Connected through hyperedge HE-2 (induced by source paper "searching for accurate binary neural architectures"):
  with members:
    - "attribute aware pooling for pedestrian attribute recognition"
    - "projection convolutional neural networks for 1 bit cnns via discrete back propagation"
    - "bi real net enhancing the performance of 1 bit cnns with improved representational capability and advanced training algorithm"
    - "regularized evolution for image classifier architecture search"
    - "wrpn wide reduced precision networks"
    - "towards accurate binary convolutional neural network"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "deep learning with low precision by half wave gaussian quantization"

Connected through hyperedge HE-3 (induced by source paper "attribute aware pooling for pedestrian attribute recognition"):
  with members:
    - "attribute aware attention model for fine grained representation learning"
    - "deep imbalanced attribute classification using visual attention aggregation"
    - "localization guided learning for pedestrian attribute recognition"
    - "hydraplus net attentive deep features for pedestrian analysis"
    - "attribute recognition by joint recurrent learning of context and correlation"
    - "beyond part models person retrieval with refined part pooling and a strong convolutional baseline"
    - "fully adaptive feature sharing in multi task networks with applications in person attribute classification"
    - "learning deep feature representations with domain guided dropout for person re identification"

Connected through hyperedge HE-4 (induced by source paper "deep feature selection using a teacher student network"):
  with members:
    - "graph autoencoder based unsupervised feature selection with broad and local data structure preservation"
    - "dropout feature ranking for deep learning models"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "learning deconvolution network for semantic segmentation"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "efficient wrapper feature selection using autoencoder and model based elimination"):
  with members:
    - "convolutional radio modulation recognition networks"

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.LG(Machine Learning)