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: "learning stylometric representations for authorship analysis"
  Abstract: Authorship analysis (AA) is the study of unveiling the hidden properties of authors from a body of exponentially exploding textual data. It extracts an author's identity and sociolinguistic characteristics based on the reflected writing styles in the text. It is an essential process for various areas, such as cybercrime investigation, psycholinguistics, political socialization, etc. However, most of the previous techniques critically depend on the manual feature engineering process. Consequently, the choice of feature set has been shown to be scenario- or dataset-dependent. In this paper, to mimic the human sentence composition process using a neural network approach, we propose to incorporate different categories of linguistic features into distributed representation of words in order to learn simultaneously the writing style representations based on unlabeled texts for authorship analysis. In particular, the proposed models allow topical, lexical, syntactical, and character-level feature vectors of each document to be extracted as stylometrics. We evaluate the performance of our approach on the problems of authorship characterization and authorship verification with the Twitter, novel, and essay datasets. The experiments suggest that our proposed text representation outperforms the bag-of-lexical-n-grams, Latent Dirichlet Allocation, Latent Semantic Analysis, PVDM, PVDBOW, and word2vec representations.

Connected through hyperedge HE-1 (induced by source paper "er ae differentially private text generation for authorship anonymization"):
  with members:
    - "author obfuscation using generalised differential privacy"
    - "unsupervised text style transfer using language models as discriminators"
    - "adversarial text generation via feature mover s distance"
    - "universal sentence encoder"
    - "content preserving text generation with attribute controls"
    - "a dataset of peer reviews peerread collection insights and nlp applications"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "syntf synthetic and differentially private term frequency vectors for privacy preserving text mining"

Connected through hyperedge HE-2 (induced by source paper "learning dynamic author representations with temporal language models"):
  with members:
    - "unsupervised text style transfer using language models as discriminators"
    - "multiple attribute text style transfer"
    - "deep contextualized word representations"
    - "an analysis of neural language modeling at multiple scales"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "construction of the literature graph in semantic scholar"
    - "dynamic word embeddings for evolving semantic discovery"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"

Connected through hyperedge HE-3 (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"
    - "author identification using multi headed recurrent neural networks"
    - "image based recommendations on styles and substitutes"

Connected through hyperedge HE-4 (induced by source paper "multifaceted privacy how to express your online persona without revealing your sensitive attributes"):
  with members:
    - "tagvisor a privacy advisor for sharing hashtags"
    - "haze privacy preserving real time traffic statistics"
    - "geo indistinguishability differential privacy for location based systems"
    - "generating sequences with recurrent neural 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.CL(Computation and Language)