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: "computational content analysis of negative tweets for obesity diet diabetes and exercise"
  Abstract: Social media based digital epidemiology has the potential to support faster response and deeper understanding of public health related threats. This study proposes a new framework to analyze unstructured health related textual data via Twitter users' post (tweets) to characterize the negative health sentiments and non-health related concerns in relations to the corpus of negative sentiments, regarding Diet Diabetes Exercise, and Obesity (DDEO). Through the collection of 6 million Tweets for one month, this study identified the prominent topics of users as it relates to the negative sentiments. Our proposed framework uses two text mining methods, sentiment analysis and topic modeling, to discover negative topics. The negative sentiments of Twitter users support the literature narratives and the many morbidity issues that are associated with DDEO and the linkage between obesity and diabetes. The framework offers a potential method to understand the publics' opinions and sentiments regarding DDEO. More importantly, this research provides new opportunities for computational social scientists, medical experts, and public health professionals to collectively address DDEO-related issues.

Connected through hyperedge HE-1 (induced by source paper "twitter speaks a case of national disaster situational awareness"):
  with members:
    - "characterizing transgender health issues in twitter"
    - "social media analysis for organizations us northeastern public and state libraries case study"
    - "characterizing diseases and disorders in gay users tweets"
    - "mining public opinion about economic issues twitter and the u s presidential election"
    - "life never matters in the democrats mind examining strategies of retweeted social bots during a mass shooting event"
    - "what do the us west coast public libraries post on twitter"
    - "applications of deep learning and reinforcement learning to biological data"
    - "deep learning for iot big data and streaming analytics a survey"

Connected through hyperedge HE-2 (induced by source paper "hidden in plain sight for too long using text mining techniques to shine a light on workplace sexism and sexual harassment"):
  with members:
    - "twitter speaks a case of national disaster situational awareness"
    - "characterizing transgender health issues in twitter"
    - "what do the us west coast public libraries post on twitter"

Connected through hyperedge HE-3 (induced by source paper "characterizing transgender health issues in twitter"):
  with members:
    - "characterizing diseases and disorders in gay users tweets"
    - "mining public opinion about economic issues twitter and the u s presidential election"
    - "you tweet what you eat studying food consumption through twitter"

Connected through hyperedge HE-4 (induced by source paper "what do the us west coast public libraries post on twitter"):
  with members:
    - "characterizing diseases and disorders in gay users tweets"
    - "mining public opinion about economic issues twitter and the u s presidential election"
    - "twitter mood predicts the stock market"

Connected through hyperedge HE-5 (induced by source paper "characterizing diseases and disorders in gay users tweets"):
  with members:
    - "you tweet what you eat studying food consumption through twitter"

Connected through hyperedge HE-6 (induced by source paper "mining public opinion about economic issues twitter and the u s presidential election"):
  with members:
    - "the predictive power of social media on the predictability of u s presidential elections using twitter"

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.SI(Social and Information Networks)