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: "inducing domain specific sentiment lexicons from unlabeled corpora"
  Abstract: A word's sentiment depends on the domain in which it is used. Computational social science research thus requires sentiment lexicons that are specific to the domains being studied. We combine domain-specific word embeddings with a label propagation framework to induce accurate domain-specific sentiment lexicons using small sets of seed words, achieving state-of-the-art performance competitive with approaches that rely on hand-curated resources. Using our framework we perform two large-scale empirical studies to quantify the extent to which sentiment varies across time and between communities. We induce and release historical sentiment lexicons for 150 years of English and community-specific sentiment lexicons for 250 online communities from the social media forum Reddit. The historical lexicons show that more than 5% of sentiment-bearing (non-neutral) English words completely switched polarity during the last 150 years, and the community-specific lexicons highlight how sentiment varies drastically between different communities.

Connected through hyperedge HE-1 (induced by source paper "generating word and document embeddings for sentiment analysis"):
  with members:
    - "a primer on neural network models for natural language processing"
    - "efficient estimation of word representations in vector space"
    - "from frequency to meaning vector space models of semantics"

Connected through hyperedge HE-2 (induced by source paper "fusing vector space models for domain specific applications"):
  with members:
    - "lifelong domain word embedding via meta learning"
    - "frustratingly easy meta embedding computing meta embeddings by averaging source word embeddings"
    - "deep contextualized word representations"
    - "phrase based neural unsupervised machine translation"
    - "comparative study of cnn and rnn for natural language processing"
    - "a simple regularization based algorithm for learning cross domain word embeddings"
    - "multichannel variable size convolution for sentence classification"
    - "enriching word vectors with subword information"

Connected through hyperedge HE-3 (induced by source paper "a resource free evaluation metric for cross lingual word embeddings based on graph modularity"):
  with members:
    - "a survey of cross lingual word embedding models"
    - "how to properly evaluate cross lingual word embeddings on strong baselines comparative analyses and some misconceptions"
    - "non adversarial unsupervised word translation"
    - "on the limitations of unsupervised bilingual dictionary induction"
    - "a robust self learning method for fully unsupervised cross lingual mappings of word embeddings"
    - "unsupervised machine translation using monolingual corpora only"
    - "unsupervised neural machine translation"
    - "word translation without parallel data"

Connected through hyperedge HE-4 (induced by source paper "ranking and selecting multi hop knowledge paths to better predict human needs"):
  with members:
    - "modeling naive psychology of characters in simple commonsense stories"
    - "deep learning for sentiment analysis a survey"
    - "knowledgeable reader enhancing cloze style reading comprehension with external commonsense knowledge"
    - "dynamic integration of background knowledge in neural nlu systems"
    - "deep contextualized word representations"
    - "zero shot sequence labeling transferring knowledge from sentences to tokens"
    - "commonsense for generative multi hop question answering tasks"
    - "reflections on sentiment opinion analysis"

Connected through hyperedge HE-5 (induced by source paper "subjective databases"):
  with members:
    - "voyageur an experiential travel search engine"
    - "deep learning for sentiment analysis a survey"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "learning a neural semantic parser from user feedback"
    - "recursive neural conditional random fields for aspect based sentiment analysis"
    - "ultradense word embeddings by orthogonal transformation"

Connected through hyperedge HE-6 (induced by source paper "time out temporal referencing for robust modeling of lexical semantic change"):
  with members:
    - "gasc genre aware semantic change for ancient greek"
    - "survey of computational approaches to lexical semantic change"
    - "dynamic word embeddings for evolving semantic discovery"
    - "diachronic word embeddings and semantic shifts a survey"
    - "on the linearity of semantic change investigating meaning variation via dynamic graph models"
    - "german in flux detecting metaphoric change via word entropy"
    - "diachronic word embeddings reveal statistical laws of semantic change"
    - "statistically significant detection of linguistic change"

Connected through hyperedge HE-7 (induced by source paper "hope speech detection a computational analysis of the voice of peace"):
  with members:
    - "something s brewing early prediction of controversy causing posts from discussion features"
    - "polyglot contextual representations improve crosslingual transfer"
    - "kashmir a computational analysis of the voice of peace"
    - "low resource parsing with crosslingual contextualized representations"
    - "polyglot semantic role labeling"
    - "forecasting the presence and intensity of hostility on instagram using linguistic and social features"
    - "mind your language abuse and offense detection for code switched languages"
    - "learning to generate reviews and discovering sentiment"

Connected through hyperedge HE-8 (induced by source paper "unisent universal adaptable sentiment lexica for 1000 languages"):
  with members:
    - "bilingual sentiment embeddings joint projection of sentiment across languages"
    - "past present future a computational investigation of the typology of tense in 1000 languages"
    - "leveraging large amounts of weakly supervised data for multi language sentiment classification"
    - "word translation without parallel data"
    - "enriching word vectors with subword information"
    - "comparing fifty natural languages and twelve genetic languages using word embedding language divergence weld as a quantitative measure of language distance"
    - "ultradense word embeddings by orthogonal transformation"
    - "statistically significant detection of linguistic change"

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)