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: "author identification using multi headed recurrent neural networks"
  Abstract: Recurrent neural networks (RNNs) are very good at modelling the flow of text, but typically need to be trained on a far larger corpus than is available for the PAN 2015 Author Identification task. This paper describes a novel approach where the output layer of a character-level RNN language model is split into several independent predictive sub-models, each representing an author, while the recurrent layer is shared by all. This allows the recurrent layer to model the language as a whole without over-fitting, while the outputs select aspects of the underlying model that reflect their author's style. The method proves competitive, ranking first in two of the four languages.

Connected through hyperedge HE-1 (induced by source paper "text analysis in adversarial settings does deception leave a stylistic trace"):
  with members:
    - "hunting for troll comments in news community forums"
    - "all you need is love evading hate speech detection"
    - "a survey on natural language processing for fake news detection"
    - "style transfer through back translation"
    - "fine tuned language models for text classification"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "dear sir or madam may i introduce the yafc corpus corpus benchmarks and metrics for formality style transfer"
    - "stay on topic generating context specific fake restaurant reviews"

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 "effective writing style imitation via combinatorial paraphrasing"):
  with members:
    - "eat2seq a generic framework for controlled sentence transformation without task specific training"
    - "style transfer through back translation"
    - "content preserving text generation with attribute controls"
    - "style transfer from non parallel text by cross alignment"
    - "recent trends in deep learning based natural language processing"
    - "paraphrase generation with deep reinforcement learning"
    - "achieving open vocabulary neural machine translation with hybrid word character models"
    - "domain specific author attribution based on feedforward neural network language models"

Connected through hyperedge HE-4 (induced by source paper "explainable authorship verification in social media via attention based similarity learning"):
  with members:
    - "unary and binary classification approaches and their implications for authorship verification"
    - "similarity learning for authorship verification in social media"
    - "attention is not explanation"
    - "explainable prediction of medical codes from clinical text"
    - "advances in pre training distributed word representations"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "learning stylometric representations for authorship analysis"
    - "image based recommendations on styles and substitutes"

Connected through hyperedge HE-5 (induced by source paper "the trumpiest trump identifying a subject s most characteristic tweets"):
  with members:
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "seismic a self exciting point process model for predicting tweet popularity"
    - "adam a method for stochastic optimization"
    - "a bayesian approach for predicting the popularity of tweets"
    - "the effect of wording on message propagation topic and author controlled natural experiments on twitter"
    - "the pulse of news in social media forecasting popularity"

Connected through hyperedge HE-6 (induced by source paper "unary and binary classification approaches and their implications for authorship verification"):
  with members:
    - "image based recommendations on styles and substitutes"
    - "one class classification taxonomy of study and review of techniques"

Connected through hyperedge HE-7 (induced by source paper "assessing the applicability of authorship verification methods"):
  with members:
    - "text analysis in adversarial settings does deception leave a stylistic trace"

Connected through hyperedge HE-8 (induced by source paper "guess who multilingual approach for the automated generation of author stylized poetry"):
  with members:
    - "what is wrong with style transfer for texts"
    - "delete retrieve generate a simple approach to sentiment and style transfer"
    - "learning to generate reviews and discovering sentiment"
    - "toward controlled generation of text"
    - "generating sentences by editing prototypes"
    - "flexible and creative chinese poetry generation using neural memory"
    - "style transfer from non parallel text by cross alignment"
    - "neural text generation a practical guide"

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)