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: "how opinions are received by online communities a case study on amazon com helpfulness votes"
  Abstract: There are many on-line settings in which users publicly express opinions. A number of these offer mechanisms for other users to evaluate these opinions; a canonical example is Amazon.com, where reviews come with annotations like "26 of 32 people found the following review helpful." Opinion evaluation appears in many off-line settings as well, including market research and political campaigns. Reasoning about the evaluation of an opinion is fundamentally different from reasoning about the opinion itself: rather than asking, "What did Y think of X?", we are asking, "What did Z think of Y's opinion of X?" Here we develop a framework for analyzing and modeling opinion evaluation, using a large-scale collection of Amazon book reviews as a dataset. We find that the perceived helpfulness of a review depends not just on its content but also but also in subtle ways on how the expressed evaluation relates to other evaluations of the same product. As part of our approach, we develop novel methods that take advantage of the phenomenon of review "plagiarism" to control for the effects of text in opinion evaluation, and we provide a simple and natural mathematical model consistent with our findings. Our analysis also allows us to distinguish among the predictions of competing theories from sociology and social psychology, and to discover unexpected differences in the collective opinion-evaluation behavior of user populations from different countries.

Connected through hyperedge HE-1 (induced by source paper "detecting vietnamese opinion spam"):
  with members:
    - "estimating continuous distributions in bayesian classifiers"
    - "finding deceptive opinion spam by any stretch of the imagination"
    - "machine learning in automated text categorization"

Connected through hyperedge HE-2 (induced by source paper "improving explainable recommendations with synthetic reviews"):
  with members:
    - "automatic generation of natural language explanations"
    - "joint deep modeling of users and items using reviews for recommendation"
    - "medical text classification using convolutional neural networks"
    - "context aware natural language generation with recurrent neural networks"
    - "image based recommendations on styles and substitutes"
    - "learning distributed representations from reviews for collaborative filtering"
    - "visualizing and understanding recurrent networks"
    - "capturing meaning in product reviews with character level generative text models"

Connected through hyperedge HE-3 (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"
    - "twitter opinion topic model extracting product opinions from tweets by leveraging hashtags and sentiment lexicon"
    - "seeing stars exploiting class relationships for sentiment categorization with respect to rating scales"

Connected through hyperedge HE-4 (induced by source paper "addressing complex and subjective product related queries with customer reviews"):
  with members:
    - "inferring networks of substitutable and complementary products"
    - "ask me anything dynamic memory networks for natural language processing"
    - "bpr bayesian personalized ranking from implicit feedback"
    - "lexrank graph based lexical centrality as salience in text summarization"

Connected through hyperedge HE-5 (induced by source paper "sentiment analysis on youtube a brief survey"):
  with members:
    - "understanding the characteristics of internet short video sharing youtube as a case study"

Connected through hyperedge HE-6 (induced by source paper "how community feedback shapes user behavior"):
  with members:
    - "estimating the prevalence of deception in online review communities"

Connected through hyperedge HE-7 (induced by source paper "you had me at hello how phrasing affects memorability"):
  with members:
    - "exploring text virality in social networks"
    - "do linguistic style and readability of scientific abstracts affect their virality"
    - "chameleons in imagined conversations a new approach to understanding coordination of linguistic style in dialogs"

Connected through hyperedge HE-8 (induced by source paper "finding deceptive opinion spam by any stretch of the imagination"):
  with members:
    - "machine learning in automated text categorization"

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)