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: "twitter opinion topic model extracting product opinions from tweets by leveraging hashtags and sentiment lexicon"
  Abstract: Aspect-based opinion mining is widely applied to review data to aggregate or summarize opinions of a product, and the current state-of-the-art is achieved with Latent Dirichlet Allocation (LDA)-based model. Although social media data like tweets are laden with opinions, their "dirty" nature (as natural language) has discouraged researchers from applying LDA-based opinion model for product review mining. Tweets are often informal, unstructured and lacking labeled data such as categories and ratings, making it challenging for product opinion mining. In this paper, we propose an LDA-based opinion model named Twitter Opinion Topic Model (TOTM) for opinion mining and sentiment analysis. TOTM leverages hashtags, mentions, emoticons and strong sentiment words that are present in tweets in its discovery process. It improves opinion prediction by modeling the target-opinion interaction directly, thus discovering target specific opinion words, neglected in existing approaches. Moreover, we propose a new formulation of incorporating sentiment prior information into a topic model, by utilizing an existing public sentiment lexicon. This is novel in that it learns and updates with the data. We conduct experiments on 9 million tweets on electronic products, and demonstrate the improved performance of TOTM in both quantitative evaluations and qualitative analysis. We show that aspect-based opinion analysis on massive volume of tweets provides useful opinions on products.

Connected through hyperedge HE-1 (induced by source paper "a joint model of conversational discourse and latent topics on microblogs"):
  with members:
    - "improving topic models with latent feature word representations"
    - "joint modeling of content and discourse relations in dialogues"
    - "jointly learning word embeddings and latent topics"
    - "tweet2vec character based distributed representations for social media"
    - "learning reporting dynamics during breaking news for rumour detection in social media"
    - "skip thought vectors"
    - "unsupervised cross domain word representation learning"
    - "distributed representations of sentences and documents"

Connected through hyperedge HE-2 (induced by source paper "estimating the rating of reviewers based on the text"):
  with members:
    - "near data scheduling for data centers with multiple levels of data locality"
    - "affinity scheduling and the applications on data center scheduling with data locality"
    - "how opinions are received by online communities a case study on amazon com helpfulness votes"
    - "seeing stars exploiting class relationships for sentiment categorization with respect to rating scales"

Connected through hyperedge HE-3 (induced by source paper "aspect sentiment model for micro reviews"):
  with members:
    - "recursive neural conditional random fields for aspect based sentiment analysis"
    - "inducing domain specific sentiment lexicons from unlabeled corpora"
    - "a hierarchical model of reviews for aspect based sentiment analysis"
    - "measuring praise and criticism inference of semantic orientation from association"

Connected through hyperedge HE-4 (induced by source paper "holistic influence maximization combining scalability and efficiency with opinion aware models"):
  with members:
    - "imrank influence maximization via finding self consistent ranking"
    - "influence maximization near optimal time complexity meets practical efficiency"
    - "viral marketing meets social advertising ad allocation with minimum regret"
    - "sketch based influence maximization and computation scaling up with guarantees"
    - "staticgreedy solving the scalability accuracy dilemma in influence maximization"
    - "maximizing social influence in nearly optimal time"
    - "influence diffusion dynamics and influence maximization in social networks with friend and foe relationships"
    - "irie scalable and robust influence maximization in social networks"

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)