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: "leveraging cognitive features for sentiment analysis"
  Abstract: Sentiments expressed in user-generated short text and sentences are nuanced by subtleties at lexical, syntactic, semantic and pragmatic levels. To address this, we propose to augment traditional features used for sentiment analysis and sarcasm detection, with cognitive features derived from the eye-movement patterns of readers. Statistical classification using our enhanced feature set improves the performance (F-score) of polarity detection by a maximum of 3.7% and 9.3% on two datasets, over the systems that use only traditional features. We perform feature significance analysis, and experiment on a held-out dataset, showing that cognitive features indeed empower sentiment analyzers to handle complex constructs.

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"
    - "what to do about non standard or non canonical language in nlp"
    - "improving sentence compression by learning to predict gaze"

Connected through hyperedge HE-2 (induced by source paper "entity recognition at first sight improving ner with eye movement information"):
  with members:
    - "neural architectures for named entity recognition"
    - "bidirectional lstm crf models for sequence tagging"
    - "named entity recognition with bidirectional lstm cnns"
    - "turkergaze crowdsourcing saliency with webcam based eye tracking"
    - "natural language processing almost from scratch"

Connected through hyperedge HE-3 (induced by source paper "dual memory network model for biased product review classification"):
  with members:
    - "question answering on knowledge bases and text using universal schema and memory networks"
    - "linguistically regularized lstms for sentiment classification"
    - "aspect level sentiment classification with deep memory network"
    - "cached long short term memory neural networks for document level sentiment classification"
    - "end to end memory networks"
    - "convolutional neural networks for sentence classification"

Connected through hyperedge HE-4 (induced by source paper "is your statement purposeless predicting computer science graduation admission acceptance based on statement of purpose"):
  with members:
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "distributed representations of words and phrases and their compositionality"
    - "semantic similarity in a taxonomy an information based measure and its application to problems of ambiguity in natural language"

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)