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: "character level and multi channel convolutional neural networks for large scale authorship attribution"
  Abstract: Convolutional neural networks (CNNs) have demonstrated superior capability for extracting information from raw signals in computer vision. Recently, character-level and multi-channel CNNs have exhibited excellent performance for sentence classification tasks. We apply CNNs to large-scale authorship attribution, which aims to determine an unknown text's author among many candidate authors, motivated by their ability to process character-level signals and to differentiate between a large number of classes, while making fast predictions in comparison to state-of-the-art approaches. We extensively evaluate CNN-based approaches that leverage word and character channels and compare them against state-of-the-art methods for a large range of author numbers, shedding new light on traditional approaches. We show that character-level CNNs outperform the state-of-the-art on four out of five datasets in different domains. Additionally, we present the first application of authorship attribution to reddit.

Connected through hyperedge HE-1 (induced by source paper "authorship attribution in bangla literature using character level cnn"):
  with members:
    - "learning to generate reviews and discovering sentiment"
    - "a joint model for word embedding and word morphology"
    - "achieving open vocabulary neural machine translation with hybrid word character models"
    - "charagram embedding words and sentences via character n grams"
    - "exploring the limits of language modeling"
    - "a character level decoder without explicit segmentation for neural machine translation"
    - "character level convolutional networks for text classification"
    - "finding function in form compositional character models for open vocabulary word representation"

Connected through hyperedge HE-2 (induced by source paper "siamese networks for large scale author identification"):
  with members:
    - "bert rediscovers the classical nlp pipeline"
    - "generalised differential privacy for text document processing"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "node2vec scalable feature learning for networks"
    - "task guided and path augmented heterogeneous network embedding for author identification"
    - "matching networks for one shot learning"

Connected through hyperedge HE-3 (induced by source paper "adapting deep learning methods for mental health prediction on social media"):
  with members:
    - "not just depressed bipolar disorder prediction on reddit"
    - "smhd a large scale resource for exploring online language usage for multiple mental health conditions"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "a short review of ethical challenges in clinical natural language processing"
    - "multi task learning for mental health using social media text"
    - "detecting and explaining crisis"
    - "depression and self harm risk assessment in online forums"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "similarity learning for authorship verification in social media"):
  with members:
    - "ups and downs modeling the visual evolution of fashion trends with one class collaborative filtering"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "a hierarchical neural autoencoder for paragraphs and documents"
    - "semi supervised sequence learning"
    - "a large annotated corpus for learning natural language inference"
    - "convolutional neural networks for sentence classification"
    - "sequence to sequence learning with neural networks"
    - "adadelta an adaptive learning rate method"

Connected through hyperedge HE-5 (induced by source paper "style aware neural model with application in authorship attribution"):
  with members:
    - "experiments with neural networks for small and large scale authorship verification"
    - "the importance of being recurrent for modeling hierarchical structure"
    - "deep rnns encode soft hierarchical syntax"
    - "liar liar pants on fire a new benchmark dataset for fake news detection"
    - "the effect of different writing tasks on linguistic style a case study of the roc story cloze task"
    - "authorship clustering using multi headed recurrent neural networks"
    - "authorship attribution using a neural network language model"
    - "authorship attribution through function word adjacency networks"

Connected through hyperedge HE-6 (induced by source paper "image based natural language understanding using 2d convolutional neural networks"):
  with members:
    - "learning longer term dependencies in rnns with auxiliary losses"
    - "neural speed reading via skim rnn"
    - "attention is all you need"
    - "learning to skim text"
    - "semantic tagging with deep residual networks"
    - "identity mappings in deep residual networks"
    - "character level convolutional network for text classification applied to chinese corpus"
    - "learning end to end goal oriented dialog"

Connected through hyperedge HE-7 (induced by source paper "generalised differential privacy for text document processing"):
  with members:
    - "privacy preserving neural representations of text"
    - "the case for being average a mediocrity approach to style masking and author obfuscation"
    - "authorship verification based on compression models"
    - "learning differentially private recurrent language models"
    - "a comparison of neural models for word ordering"
    - "c sanitized a privacy model for document redaction and sanitization"
    - "geo indistinguishability differential privacy for location based systems"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-8 (induced by source paper "umdsub at semeval 2018 task 2 multilingual emoji prediction multi channel convolutional neural network on subword embedding"):
  with members:
    - "enriching word vectors with subword information"
    - "efficient character level document classification by combining convolution and recurrent layers"
    - "character aware neural language models"
    - "neural machine translation of rare words with subword units"
    - "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.CL(Computation and Language)