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: "large scale multi label text classification revisiting neural networks"
  Abstract: Neural networks have recently been proposed for multi-label classification because they are able to capture and model label dependencies in the output layer. In this work, we investigate limitations of BP-MLL, a neural network (NN) architecture that aims at minimizing pairwise ranking error. Instead, we propose to use a comparably simple NN approach with recently proposed learning techniques for large-scale multi-label text classification tasks. In particular, we show that BP-MLL's ranking loss minimization can be efficiently and effectively replaced with the commonly used cross entropy error function, and demonstrate that several advances in neural network training that have been developed in the realm of deep learning can be effectively employed in this setting. Our experimental results show that simple NN models equipped with advanced techniques such as rectified linear units, dropout, and AdaGrad perform as well as or even outperform state-of-the-art approaches on six large-scale textual datasets with diverse characteristics.

Connected through hyperedge HE-1 (induced by source paper "learning visual actions using multiple verb only labels"):
  with members:
    - "action2vec a crossmodal embedding approach to action learning"
    - "scaling egocentric vision the epic kitchens dataset"
    - "end to end dense video captioning with masked transformer"
    - "reconstruction network for video captioning"
    - "what actions are needed for understanding human actions in videos"
    - "video captioning via hierarchical reinforcement learning"
    - "convolutional image captioning"
    - "zero shot activity recognition with verb attribute induction"

Connected through hyperedge HE-2 (induced by source paper "a multi label classification method using a hierarchical and transparent representation for paper reviewer recommendation"):
  with members:
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "multi label classification of patient notes a case study on icd code assignment"
    - "deep sentence embedding using long short term memory networks analysis and application to information retrieval"
    - "distributed representations of sentences and documents"
    - "neural machine translation by jointly learning to align and translate"
    - "speech recognition with deep recurrent neural networks"
    - "efficient estimation of word representations in vector space"
    - "a framework for optimizing paper matching"

Connected through hyperedge HE-3 (induced by source paper "multi label learning with deep forest"):
  with members:
    - "distributed deep forest and its application to automatic detection of cash out fraud"
    - "deep forest"
    - "deep forest towards an alternative to deep neural networks"
    - "autoencoder by forest"
    - "a unified view of multi label performance measures"
    - "cnn rnn a unified framework for multi label image classification"
    - "deep semantic ranking based hashing for multi label image retrieval"
    - "top down induction of clustering trees"

Connected through hyperedge HE-4 (induced by source paper "on using machine learning to identify knowledge in api reference documentation"):
  with members:
    - "sentiment polarity detection for software development"
    - "generalized cross entropy loss for training deep neural networks with noisy labels"
    - "natural language or not nlon a package for software engineering text analysis pipeline"
    - "variants of rmsprop and adagrad with logarithmic regret bounds"
    - "easy over hard a case study on deep learning"
    - "an overview of gradient descent optimization algorithms"
    - "distributed representations of words and phrases and their compositionality"
    - "what should developers be aware of an empirical study on the directives of api documentation"

Connected through hyperedge HE-5 (induced by source paper "ml net multi label classification of biomedical texts with deep neural networks"):
  with members:
    - "tienet text image embedding network for common thorax disease classification and reporting in chest x rays"
    - "multi label classification of patient notes a case study on icd code assignment"
    - "improving pairwise ranking for multi label image classification"
    - "adam a method for stochastic optimization"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-6 (induced by source paper "using deep learning for title based semantic subject indexing to reach competitive performance to full text"):
  with members:
    - "lstm a search space odyssey"
    - "on the state of the art of evaluation in neural language models"
    - "amplifying the impact of open access wikipedia and the diffusion of science"
    - "do convolutional networks need to be deep for text classification"
    - "comparative study of cnn and rnn for natural language processing"
    - "recent trends in deep learning based natural language processing"
    - "deep extreme multi label learning"
    - "using titles vs full text as source for automated semantic document annotation"

Connected through hyperedge HE-7 (induced by source paper "joint binary neural network for multi label learning with applications to emotion classification"):
  with members:
    - "deep neural networks for czech multi label document classification"
    - "deep learning for multi label classification"
    - "neural machine translation by jointly learning to align and translate"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-8 (induced by source paper "incorporating word embeddings into open directory project based large scale classification"):
  with members:
    - "distributed representations of sentences and documents"
    - "convolutional neural networks for sentence classification"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

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)