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: "experiential distributional and dependency based word embeddings have complementary roles in decoding brain activity"
  Abstract: We evaluate 8 different word embedding models on their usefulness for predicting the neural activation patterns associated with concrete nouns. The models we consider include an experiential model, based on crowd-sourced association data, several popular neural and distributional models, and a model that reflects the syntactic context of words (based on dependency parses). Our goal is to assess the cognitive plausibility of these various embedding models, and understand how we can further improve our methods for interpreting brain imaging data. #R##N#We show that neural word embedding models exhibit superior performance on the tasks we consider, beating experiential word representation model. The syntactically informed model gives the overall best performance when predicting brain activation patterns from word embeddings; whereas the GloVe distributional method gives the overall best performance when predicting in the reverse direction (words vectors from brain images). Interestingly, however, the error patterns of these different models are markedly different. This may support the idea that the brain uses different systems for processing different kinds of words. Moreover, we suggest that taking the relative strengths of different embedding models into account will lead to better models of the brain activity associated with words.

Connected through hyperedge HE-1 (induced by source paper "the fluidity of concept representations in human brain signals"):
  with members:
    - "robust evaluation of language brain encoding experiments"
    - "analyzing and interpreting neural networks for nlp a report on the first blackboxnlp workshop"
    - "advancing nlp with cognitive language processing signals"
    - "finding syntax in human encephalography with beam search"
    - "does the brain represent words an evaluation of brain decoding studies of language understanding"
    - "multimodal grounding for language processing"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-2 (induced by source paper "cognival a framework for cognitive word embedding evaluation"):
  with members:
    - "robust evaluation of language brain encoding experiments"
    - "entity recognition at first sight improving ner with eye movement information"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "blackbox meets blackbox representational similarity and stability analysis of neural language models and brains"):
  with members:
    - "robust evaluation of language brain encoding experiments"
    - "correlating neural and symbolic representations of language"
    - "under the hood using diagnostic classifiers to investigate and improve how language models track agreement information"
    - "gromov wasserstein alignment of word embedding spaces"
    - "deep contextualized word representations"
    - "towards understanding learning representations to what extent do different neural networks learn the same representation"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "sharp nearby fuzzy far away how neural language models use context"

Connected through hyperedge HE-4 (induced by source paper "expertocoder capturing divergent brain regions using mixture of regression experts"):
  with members:
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-5 (induced by source paper "robust evaluation of language brain encoding experiments"):
  with members:
    - "does the brain represent words an evaluation of brain decoding studies of language understanding"

Connected through hyperedge HE-6 (induced by source paper "a survey of word embeddings evaluation methods"):
  with members:
    - "semantic structure and interpretability of word embeddings"
    - "word embeddings quantify 100 years of gender and ethnic stereotypes"
    - "an exploration of word embedding initialization in deep learning tasks"
    - "hypothesis testing based intrinsic evaluation of word embeddings"
    - "how to evaluate word embeddings on importance of data efficiency and simple supervised tasks"
    - "automated detection of non relevant posts on the russian imageboard 2ch importance of the choice of word representations"
    - "cross lingual models of word embeddings an empirical comparison"
    - "a proposal for linguistic similarity datasets based on commonality lists"

Connected through hyperedge HE-7 (induced by source paper "does the brain represent words an evaluation of brain decoding studies of language understanding"):
  with members:
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "dissent sentence representation learning from explicit discourse relations"
    - "skip thought vectors"
    - "order embeddings of images and 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)