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: "label embedding network learning label representation for soft training of deep networks"
  Abstract: We propose a method, called Label Embedding Network, which can learn label representation (label embedding) during the training process of deep networks. With the proposed method, the label embedding is adaptively and automatically learned through back propagation. The original one-hot represented loss function is converted into a new loss function with soft distributions, such that the originally unrelated labels have continuous interactions with each other during the training process. As a result, the trained model can achieve substantially higher accuracy and with faster convergence speed. Experimental results based on competitive tasks demonstrate the effectiveness of the proposed method, and the learned label embedding is reasonable and interpretable. The proposed method achieves comparable or even better results than the state-of-the-art systems.

Connected through hyperedge HE-1 (induced by source paper "hierarchy based image embeddings for semantic image retrieval"):
  with members:
    - "random erasing data augmentation"
    - "beyond triplet loss a deep quadruplet network for person re identification"
    - "learning structured semantic embeddings for visual recognition"
    - "significance of softmax based features in comparison to distance metric learning based features"
    - "sgdr stochastic gradient descent with restarts"
    - "yolo9000 better faster stronger"
    - "densely connected convolutional networks"
    - "deep pyramidal residual networks"

Connected through hyperedge HE-2 (induced by source paper "shrewd semantic hierarchy based relational embeddings for weakly supervised deep hashing"):
  with members:
    - "semantic hierarchy preserving deep hashing for large scale image retrieval"
    - "hierarchy based image embeddings for semantic image retrieval"
    - "dual asymmetric deep hashing learning"
    - "mean local group average precision mlgap a new performance metric for hashing based retrieval"
    - "unsupervised semantic deep hashing"
    - "hashnet deep learning to hash by continuation"
    - "supervised deep hashing for hierarchical labeled data"
    - "a survey on learning to hash"

Connected through hyperedge HE-3 (induced by source paper "deconvolution based global decoding for neural machine translation"):
  with members:
    - "global encoding for abstractive summarization"
    - "decoding history based adaptive control of attention for neural machine translation"
    - "bag of words as target for neural machine translation"
    - "attention is all you need"
    - "deconvolutional latent variable model for text sequence matching"
    - "get to the point summarization with pointer generator networks"
    - "convolutional sequence to sequence learning"
    - "deconvolutional paragraph representation learning"

Connected through hyperedge HE-4 (induced by source paper "global semantic consistency for zero shot learning"):
  with members:
    - "preserving semantic relations for zero shot learning"
    - "semantic autoencoder for zero shot learning"
    - "zero shot learning a comprehensive evaluation of the good the bad and the ugly"
    - "learning to compare relation network for few shot learning"
    - "semantically consistent regularization for zero shot recognition"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "zero shot learning the good the bad and the ugly"
    - "recovering the missing link predicting class attribute associations for unsupervised zero shot learning"

Connected through hyperedge HE-5 (induced by source paper "exploration on grounded word embedding matching words and images with image enhanced skip gram model"):
  with members:
    - "simnet stepwise image topic merging network for generating detailed and comprehensive image captions"
    - "zero resource neural machine translation with multi agent communication game"
    - "regularizing output distribution of abstractive chinese social media text summarization for improved semantic consistency"
    - "query and output generating words by querying distributed word representations for paraphrase generation"
    - "image pivoting for learning multilingual multimodal representations"
    - "exploring models and data for image question answering"
    - "learning deep structure preserving image text embeddings"
    - "multimodal convolutional neural networks for matching image and sentence"

Connected through hyperedge HE-6 (induced by source paper "sgm sequence generation model for multi label classification"):
  with members:
    - "global encoding for abstractive summarization"
    - "bag of words as target for neural machine translation"
    - "style transfer from non parallel text by cross alignment"
    - "a scikit based python environment for performing multi label classification"
    - "how is a data driven approach better than random choice in label space division for multi label classification"
    - "a neural attention model for abstractive sentence summarization"
    - "effective approaches to attention based neural machine translation"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "a hierarchical end to end model for jointly improving text summarization and sentiment classification"):
  with members:
    - "get to the point summarization with pointer generator networks"
    - "ups and downs modeling the visual evolution of fashion trends with one class collaborative filtering"
    - "lcsts a large scale chinese short text summarization dataset"
    - "adam a method for stochastic optimization"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-8 (induced by source paper "improving social media text summarization by learning sentence weight distribution"):
  with members:
    - "improving semantic relevance for sequence to sequence learning of chinese social media text summarization"
    - "transfer deep learning for low resource chinese word segmentation with a novel neural network"
    - "abstractive text summarization using sequence to sequence rnns and beyond"
    - "incorporating copying mechanism in sequence to sequence learning"
    - "neural summarization by extracting sentences and words"
    - "a neural attention model for abstractive sentence summarization"
    - "lcsts a large scale chinese short text summarization dataset"
    - "abstractive multi document summarization via phrase selection and merging"

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)