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: "user level sentiment analysis incorporating social networks"
  Abstract: We show that information about social relationships can be used to improve user-level sentiment analysis. The main motivation behind our approach is that users that are somehow "connected" may be more likely to hold similar opinions; therefore, relationship information can complement what we can extract about a user's viewpoints from their utterances. Employing Twitter as a source for our experimental data, and working within a semi-supervised framework, we propose models that are induced either from the Twitter follower/followee network or from the network in Twitter formed by users referring to each other using "@" mentions. Our transductive learning results reveal that incorporating social-network information can indeed lead to statistically significant sentiment-classification improvements over the performance of an approach based on Support Vector Machines having access only to textual features.

Connected through hyperedge HE-1 (induced by source paper "jnet learning user representations via joint network embedding and topic embedding"):
  with members:
    - "node2vec scalable feature learning for networks"
    - "line large scale information network embedding"
    - "deepwalk online learning of social representations"
    - "probabilistic latent semantic analysis"
    - "efficient estimation of word representations in vector space"
    - "representation learning a review and new perspectives"
    - "the author topic model for authors and documents"

Connected through hyperedge HE-2 (induced by source paper "rethinking attribute representation and injection for sentiment classification"):
  with members:
    - "categorical metadata representation for customized text classification"
    - "dual memory network model for biased product review classification"
    - "summarizing opinions aspect extraction meets sentiment prediction and they are both weakly supervised"
    - "translations as additional contexts for sentence classification"
    - "improving review representations with user attention and product attention for sentiment classification"
    - "ordered neurons integrating tree structures into recurrent neural networks"
    - "cold start aware user and product attention for sentiment classification"
    - "extreme adaptation for personalized neural machine translation"

Connected through hyperedge HE-3 (induced by source paper "how did the discussion go discourse act classification in social media conversations"):
  with members:
    - "friends and enemies of clinton and trump using context for detecting stance in political tweets"
    - "topic independent identification of agreement and disagreement in social media dialogue"
    - "metrics for community analysis a survey"
    - "distributed representations of sentences and documents"
    - "sequence to sequence learning with neural networks"
    - "a convolutional neural network for modelling sentences"
    - "distributed representations of words and phrases and their compositionality"
    - "recurrent convolutional neural networks for discourse compositionality"

Connected through hyperedge HE-4 (induced by source paper "a heterogeneous graphical model to understand user level sentiments in social media"):
  with members:
    - "an unsupervised domain independent framework for automated detection of persuasion tactics in text"
    - "event outcome prediction using sentiment analysis and crowd wisdom in microblog feeds"
    - "object sensitive deep reinforcement learning"
    - "transparency and explanation in deep reinforcement learning neural networks"
    - "photorealistic style transfer for videos"
    - "joint embedding of hierarchical categories and entities for concept categorization and dataless classification"
    - "content based video indexing and retrieval using corr lda"
    - "thumbs up sentiment classification using machine learning techniques"

Connected through hyperedge HE-5 (induced by source paper "event outcome prediction using sentiment analysis and crowd wisdom in microblog feeds"):
  with members:
    - "object sensitive deep reinforcement learning"
    - "transparency and explanation in deep reinforcement learning neural networks"
    - "photorealistic style transfer for videos"
    - "joint embedding of hierarchical categories and entities for concept categorization and dataless classification"
    - "content based video indexing and retrieval using corr lda"
    - "distributed representations of sentences and documents"
    - "why watching movie tweets won t tell the whole story"

Connected through hyperedge HE-6 (induced by source paper "a framework for fake review detection in online consumer electronics retailers"):
  with members:
    - "bag of tricks for efficient text classification"
    - "word2vec explained deriving mikolov et al s negative sampling word embedding method"
    - "finding deceptive opinion spam by any stretch of the imagination"

Connected through hyperedge HE-7 (induced by source paper "predicting twitter user socioeconomic attributes with network and language information"):
  with members:
    - "node2vec scalable feature learning for networks"
    - "probabilistic inference of twitter users age based on what they follow"
    - "line large scale information network embedding"
    - "deepwalk online learning of social representations"
    - "distributed representations of words and phrases and their compositionality"
    - "social networks that matter twitter under the microscope"

Connected through hyperedge HE-8 (induced by source paper "stylistic variation in social media part of speech tagging"):
  with members:
    - "toward socially infused information extraction embedding authors mentions and entities"
    - "finding function in form compositional character models for open vocabulary word representation"
    - "line large scale information network embedding"
    - "adam a method for stochastic optimization"
    - "social networks that matter twitter under the microscope"

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)