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: "bleaching text abstract features for cross lingual gender prediction"
  Abstract: Gender prediction has typically focused on lexical and social network features, yielding good performance, but making systems highly language-, topic-, and platform-dependent. Cross-lingual embeddings circumvent some of these limitations, but capture gender-specific style less. We propose an alternative: bleaching text, i.e., transforming lexical strings into more abstract features. This study provides evidence that such features allow for better transfer across languages. Moreover, we present a first study on the ability of humans to perform cross-lingual gender prediction. We find that human predictive power proves similar to that of our bleached models, and both perform better than lexical models.

Connected through hyperedge HE-1 (induced by source paper "demographic inference and representative population estimates from multilingual social media data"):
  with members:
    - "on the convergence of adam and beyond"
    - "co training for demographic classification using deep learning from label proportions"
    - "word translation without parallel data"
    - "densely connected convolutional networks"
    - "a character level decoder without explicit segmentation for neural machine translation"
    - "whom should we sense in social sensing analyzing which users work best for social media now casting"
    - "character aware neural language models"
    - "morphological inflection generation using character sequence to sequence learning"

Connected through hyperedge HE-2 (induced by source paper "conan counter narratives through nichesourcing a multilingual dataset of responses to fight online hate speech"):
  with members:
    - "training millions of personalized dialogue agents"
    - "examining a hate speech corpus for hate speech detection and popularity prediction"
    - "hate speech dataset from a white supremacy forum"
    - "thou shalt not hate countering online hate speech"
    - "peer to peer hate hate speech instigators and their targets"
    - "cross lingual transfer learning for multilingual task oriented dialog"
    - "analyzing the hate and counter speech accounts on twitter"
    - "automated hate speech detection and the problem of offensive language"

Connected through hyperedge HE-3 (induced by source paper "sentence level bert and multi task learning of age and gender in social media"):
  with members:
    - "arap tweet a large multi dialect twitter corpus for gender age and language variety identification"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "learned in translation contextualized word vectors"
    - "attention correctness in neural image captioning"
    - "multi task sequence to sequence learning"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "you write like you eat stylistic variation as a predictor of social stratification"):
  with members:
    - "reporting score distributions makes a difference performance study of lstm networks for sequence tagging"
    - "understanding and overcoming biases in customer reviews"
    - "multilingual part of speech tagging with bidirectional long short term memory models and auxiliary loss"
    - "distant supervision for emotion detection using facebook reactions"
    - "learning when to trust distant supervision an application to low resource pos tagging using cross lingual projection"
    - "computational sociolinguistics a survey"
    - "adam a method for stochastic optimization"
    - "the readability of tweets and their geographic correlation with education"

Connected through hyperedge HE-5 (induced by source paper "gender prediction from tweets improving neural representations with hand crafted features"):
  with members:
    - "adam a method for stochastic optimization"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"
    - "neural machine translation by jointly learning to align and translate"

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)