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: "entity recognition at first sight improving ner with eye movement information"
  Abstract: Previous research shows that eye-tracking data contains information about the lexical and syntactic properties of text, which can be used to improve natural language processing models. In this work, we leverage eye movement features from three corpora with recorded gaze information to augment a state-of-the-art neural model for named entity recognition (NER) with gaze embeddings. These corpora were manually annotated with named entity labels. Moreover, we show how gaze features, generalized on word type level, eliminate the need for recorded eye-tracking data at test time. The gaze-augmented models for NER using token-level and type-level features outperform the baselines. We present the benefits of eye-tracking features by evaluating the NER models on both individual datasets as well as in cross-domain settings.

Connected through hyperedge HE-1 (induced by source paper "cognival a framework for cognitive word embedding evaluation"):
  with members:
    - "robust evaluation of language brain encoding experiments"
    - "fair is better than sensational man is to doctor as woman is to doctor"
    - "can eye movement data be used as ground truth for word embeddings evaluation"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a survey of word embeddings evaluation methods"
    - "replicability analysis for natural language processing testing significance with multiple datasets"
    - "experiential distributional and dependency based word embeddings have complementary roles in decoding brain activity"

Connected through hyperedge HE-2 (induced by source paper "advancing nlp with cognitive language processing signals"):
  with members:
    - "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"
    - "what to do about non standard or non canonical language in nlp"
    - "improving sentence compression by learning to predict gaze"

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)