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: "separated by an un common language towards judgment language informed vector space modeling"
  Abstract: A common evaluation practice in the vector space models (VSMs) literature is to measure the models' ability to predict human judgments about lexical semantic relations between word pairs. Most existing evaluation sets, however, consist of scores collected for English word pairs only, ignoring the potential impact of the judgment language in which word pairs are presented on the human scores. In this paper we translate two prominent evaluation sets, wordsim353 (association) and SimLex999 (similarity), from English to Italian, German and Russian and collect scores for each dataset from crowdworkers fluent in its language. Our analysis reveals that human judgments are strongly impacted by the judgment language. Moreover, we show that the predictions of monolingual VSMs do not necessarily best correlate with human judgments made with the language used for model training, suggesting that models and humans are affected differently by the language they use when making semantic judgments. Finally, we show that in a large number of setups, multilingual VSM combination results in improved correlations with human judgments, suggesting that multilingualism may partially compensate for the judgment language effect on human judgments.

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 "linspector multilingual probing tasks for word representations"):
  with members:
    - "linspector web a multilingual probing suite for word representations"
    - "a survey of cross lingual word embedding models"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "towards better ud parsing deep contextualized word embeddings ensemble and treebank concatenation"
    - "xnli evaluating cross lingual sentence representations"
    - "on the complexity and typology of inflectional morphological systems"
    - "allennlp a deep semantic natural language processing platform"
    - "unimorph 2 0 universal morphology"

Connected through hyperedge HE-3 (induced by source paper "a general framework for implicit and explicit debiasing of distributional word vector spaces"):
  with members:
    - "a survey of cross lingual word embedding models"
    - "are we consistently biased multidimensional analysis of biases in distributional word vectors"
    - "black is to criminal as caucasian is to police detecting and removing multiclass bias in word embeddings"
    - "attenuating bias in word vectors"
    - "how to properly evaluate cross lingual word embeddings on strong baselines comparative analyses and some misconceptions"
    - "gender bias in contextualized word embeddings"
    - "lipstick on a pig debiasing methods cover up systematic gender biases in word embeddings but do not remove them"
    - "gender bias in coreference resolution"

Connected through hyperedge HE-4 (induced by source paper "how does grammatical gender affect noun representations in gender marking languages"):
  with members:
    - "lipstick on a pig debiasing methods cover up systematic gender biases in word embeddings but do not remove them"
    - "counterfactual data augmentation for mitigating gender stereotypes in languages with rich morphology"
    - "demorphy german language morphological analyzer"
    - "on the limitations of unsupervised bilingual dictionary induction"
    - "learning gender neutral word embeddings"
    - "semantics derived automatically from language corpora contain human like biases"
    - "morph fitting fine tuning word vector spaces with simple language specific rules"
    - "the interplay of semantics and morphology in word embeddings"

Connected through hyperedge HE-5 (induced by source paper "from word to sense embeddings a survey on vector representations of meaning"):
  with members:
    - "a survey of cross lingual word embedding models"
    - "towards better ud parsing deep contextualized word embeddings ensemble and treebank concatenation"
    - "seven augmenting word embeddings with unsupervised relation vectors"
    - "incorporating glosses into neural word sense disambiguation"
    - "deep contextualized word representations"
    - "wic 10 000 example pairs for evaluating context sensitive representations"
    - "knowledge based word sense disambiguation using topic models"
    - "a robust self learning method for fully unsupervised cross lingual mappings of word embeddings"

Connected through hyperedge HE-6 (induced by source paper "improving cross lingual word embeddings by meeting in the middle"):
  with members:
    - "a survey of cross lingual word embedding models"
    - "frustratingly easy meta embedding computing meta embeddings by averaging source word embeddings"
    - "robust cross lingual hypernymy detection using dependency context"
    - "generalizing procrustes analysis for better bilingual dictionary induction"
    - "on the limitations of unsupervised bilingual dictionary induction"
    - "unsupervised machine translation using monolingual corpora only"
    - "semantic specialisation of distributional word vector spaces using monolingual and cross lingual constraints"
    - "word translation without parallel data"

Connected through hyperedge HE-7 (induced by source paper "adversarial propagation and zero shot cross lingual transfer of word vector specialization"):
  with members:
    - "modeling language variation and universals a survey on typological linguistics for natural language processing"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "specialising word vectors for lexical entailment"
    - "cross lingual models of word embeddings an empirical comparison"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "improved techniques for training gans"
    - "generative adversarial text to image synthesis"
    - "adversarial deep averaging networks for cross lingual sentiment classification"

Connected through hyperedge HE-8 (induced by source paper "post specialisation retrofitting vectors of words unseen in lexical resources"):
  with members:
    - "self normalizing neural networks"
    - "sigmoid weighted linear units for neural network function approximation in reinforcement learning"
    - "specialising word vectors for lexical entailment"
    - "word translation without parallel data"
    - "cross lingual induction and transfer of verb classes based on word vector space specialisation"
    - "continuously learning neural dialogue management"
    - "neural belief tracker data driven dialogue state tracking"
    - "adam a method for stochastic optimization"

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)