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: "what to do about non standard or non canonical language in nlp"
  Abstract: Real world data differs radically from the benchmark corpora we use in natural language processing (NLP). As soon as we apply our technologies to the real world, performance drops. The reason for this problem is obvious: NLP models are trained on samples from a limited set of canonical varieties that are considered standard, most prominently English newswire. However, there are many dimensions, e.g., socio-demographics, language, genre, sentence type, etc. on which texts can differ from the standard. The solution is not obvious: we cannot control for all factors, and it is not clear how to best go beyond the current practice of training on homogeneous data from a single domain and language. #R##N#In this paper, I review the notion of canonicity, and how it shapes our community's approach to language. I argue for leveraging what I call fortuitous data, i.e., non-obvious data that is hitherto neglected, hidden in plain sight, or raw data that needs to be refined. If we embrace the variety of this heterogeneous data by combining it with proper algorithms, we will not only produce more robust models, but will also enable adaptive language technology capable of addressing natural language variation.

Connected through hyperedge HE-1 (induced by source paper "advancing nlp with cognitive language processing signals"):
  with members:
    - "entity recognition at first sight improving ner with eye movement information"
    - "finding syntax in human encephalography with beam search"
    - "eth ds3lab at semeval 2018 task 7 effectively combining recurrent and convolutional neural networks for relation classification and extraction"
    - "replicability analysis for natural language processing testing significance with multiple datasets"
    - "assessing state of the art sentiment models on state of the art sentiment datasets"
    - "identifying beneficial task relations for multi task learning in deep neural networks"
    - "leveraging cognitive features for sentiment analysis"
    - "improving sentence compression by learning to predict gaze"

Connected through hyperedge HE-2 (induced by source paper "leveraging newswire treebanks for parsing conversational data with argument scrambling"):
  with members:
    - "simple and accurate dependency parsing using bidirectional lstm feature representations"
    - "improving neural networks by preventing co adaptation of feature detectors"

Connected through hyperedge HE-3 (induced by source paper "distance based source domain selection for sentiment classification"):
  with members:
    - "data selection strategies for multi domain sentiment analysis"
    - "optimal transport for domain adaptation"
    - "distributed representations of sentences and documents"
    - "efficient estimation of word representations in vector space"
    - "marginalized denoising autoencoders for domain adaptation"
    - "twitter mood predicts the stock market"
    - "on integral probability metrics φ divergences and binary classification"

Connected through hyperedge HE-4 (induced by source paper "data selection strategies for multi domain sentiment analysis"):
  with members:
    - "knowledge adaptation teaching to adapt"
    - "towards a continuous modeling of natural language domains"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "keystroke dynamics as signal for shallow syntactic parsing"):
  with members:
    - "multilingual part of speech tagging with bidirectional long short term memory models and auxiliary loss"
    - "improving sentence compression by learning to predict gaze"
    - "multilingual language processing from bytes"
    - "finding function in form compositional character models for open vocabulary word representation"
    - "improved transition based parsing by modeling characters instead of words with lstms"
    - "bidirectional lstm crf models for sequence tagging"
    - "part of speech tagging with bidirectional long short term memory recurrent neural network"
    - "transition based dependency parsing with stack long short term memory"

Connected through hyperedge HE-6 (induced by source paper "when silver glitters more than gold bootstrapping an italian part of speech tagger for twitter"):
  with members:
    - "multilingual part of speech tagging with bidirectional long short term memory models and auxiliary loss"
    - "improved transition based parsing by modeling characters instead of words with lstms"
    - "polyglot distributed word representations for multilingual nlp"
    - "natural language processing almost from scratch"
    - "tnt a statistical part of speech tagger"

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)