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: "from frequency to meaning vector space models of semantics"
  Abstract: Computers understand very little of the meaning of human language. This profoundly limits our ability to give instructions to computers, the ability of computers to explain their actions to us, and the ability of computers to analyse and process text. Vector space models (VSMs) of semantics are beginning to address these limits. This paper surveys the use of VSMs for semantic processing of text. We organize the literature on VSMs according to the structure of the matrix in a VSM. There are currently three broad classes of VSMs, based on term-document, word-context, and pair-pattern matrices, yielding three classes of applications. We survey a broad range of applications in these three categories and we take a detailed look at a specific open source project in each category. Our goal in this survey is to show the breadth of applications of VSMs for semantics, to provide a new perspective on VSMs for those who are already familiar with the area, and to provide pointers into the literature for those who are less familiar with the field.

Connected through hyperedge HE-1 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity"):
  with members:
    - "multilingual alignment of contextual word representations"
    - "emerging cross lingual structure in pretrained language models"
    - "an empirical study on post processing methods for word embeddings"
    - "informing unsupervised pretraining with external linguistic knowledge"
    - "on the robustness of unsupervised and semi supervised cross lingual word embedding learning"
    - "word similarity datasets for thai construction and evaluation"
    - "paws paraphrase adversaries from word scrambling"
    - "cross lingual ability of multilingual bert an empirical study"

Connected through hyperedge HE-2 (induced by source paper "humpty dumpty controlling word meanings via corpus poisoning"):
  with members:
    - "towards a robust deep neural network in text domain a survey"
    - "universal adversarial triggers for attacking and analyzing nlp"
    - "cross lingual alignment of contextual word embeddings with applications to zero shot dependency parsing"
    - "understanding the origins of bias in word embeddings"
    - "adversarial attacks on node embeddings via graph poisoning"
    - "black box generation of adversarial text sequences to evade deep learning classifiers"
    - "deep text classification can be fooled"
    - "towards understanding linear word analogies"

Connected through hyperedge HE-3 (induced by source paper "sentiment analysis with contextual embeddings and self attention"):
  with members:
    - "self attention with relative position representations"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "universal language model fine tuning for text classification"
    - "self attention a better building block for sentiment analysis neural network classifiers"
    - "attention is all you need"
    - "learned in translation contextualized word vectors"
    - "a structured self attentive sentence embedding"

Connected through hyperedge HE-4 (induced by source paper "distributional semantic modeling a revised technique to train term word vector space models applying the ontology related approach"):
  with members:
    - "development of formal models algorithms procedures engineering and functioning of the software system instrumental complex for ontological engineering purpose"
    - "deep contextualized word representations"
    - "integrated tools for engineering ontologies"
    - "to the problem of the instrumental complex for ontological engineering purpose software system design"
    - "research and development workstation environment the new class of current research information systems"
    - "word2vec applied to recommendation hyperparameters matter"
    - "technique for designing a domain ontology"
    - "fasttext zip compressing text classification models"

Connected through hyperedge HE-5 (induced by source paper "advances in machine learning for the behavioral sciences"):
  with members:
    - "using machine learning to guide cognitive modeling a case study in moral reasoning"
    - "wordnet feelings a linguistic categorisation of human feelings"
    - "mlic a maxsat based framework for learning interpretable classification rules"
    - "the interpretation of dream meaning resolving ambiguity using latent semantic analysis in a small corpus of text"
    - "semantics derived automatically from language corpora contain human like biases"
    - "on the latent variable interpretation in sum product networks"
    - "why should i trust you explaining the predictions of any classifier"
    - "deep learning in neural networks"

Connected through hyperedge HE-6 (induced by source paper "multiplex word embeddings for selectional preference acquisition"):
  with members:
    - "incorporating context and external knowledge for pronoun coreference resolution"
    - "knowledge aware pronoun coreference resolution"
    - "sp 10k a large scale evaluation set for selectional preference acquisition"
    - "deep contextualized word representations"
    - "higher order coreference resolution with coarse to fine inference"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "measuring thematic fit with distributional feature overlap"
    - "dynamics of core of language vocabulary"

Connected through hyperedge HE-7 (induced by source paper "tedl a text encryption method based on deep learning"):
  with members:
    - "from word to sense embeddings a survey on vector representations of meaning"
    - "local explanation methods for deep neural networks lack sensitivity to parameter values"
    - "the secret sharer measuring unintended neural network memorization extracting secrets"
    - "lightweight efficient multi keyword ranked search over encrypted cloud data using dual word embeddings"
    - "recent trends in deep learning based natural language processing"
    - "interpretable explanations of black boxes by meaningful perturbation"
    - "membership inference attacks against machine learning models"
    - "towards biologically plausible deep learning"

Connected through hyperedge HE-8 (induced by source paper "generating word and document embeddings for sentiment analysis"):
  with members:
    - "inducing domain specific sentiment lexicons from unlabeled corpora"
    - "a primer on neural network models for natural language processing"
    - "efficient estimation of word representations in vector space"

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)