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: "effect of values and technology use on exercise implications for personalized behavior change interventions"
  Abstract: Technology has recently been recruited in the war against the ongoing obesity crisis; however, the adoption of Health & Fitness applications for regular exercise is a struggle. In this study, we present a unique demographically representative dataset of 15k US residents that combines technology use logs with surveys on moral views, human values, and emotional contagion. Combining these data, we provide a holistic view of individuals to model their physical exercise behavior. First, we show which values determine the adoption of Health & Fitness mobile applications, finding that users who prioritize the value of purity and de-emphasize values of conformity, hedonism, and security are more likely to use such apps. Further, we achieve a weighted AUROC of .673 in predicting whether individual exercises, and we also show that the application usage data allows for substantially better classification performance (.608) compared to using basic demographics (.513) or internet browsing data (.546). We also find a strong link of exercise to respondent socioeconomic status, as well as the value of happiness. Using these insights, we propose actionable design guidelines for persuasive technologies targeting health behavior modification.

Connected through hyperedge HE-1 (induced by source paper "facebook ads as a demographic tool to measure the urban rural divide"):
  with members:
    - "human values and attitudes towards vaccination in social media"
    - "analyzing gender inequality through large scale facebook advertising data"
    - "predicting demographics moral foundations and human values from digital behaviours"
    - "parents mention sons more often than daughters on social media"
    - "demographic differentials in facebook usage around the world"
    - "a total error framework for digital traces of humans"
    - "mater certa est pater numquam what can facebook advertising data tell us about male fertility rates"
    - "facebook use of sensitive data for advertising in europe"

Connected through hyperedge HE-2 (induced by source paper "moralstrength exploiting a moral lexicon and embedding similarity for moral foundations prediction"):
  with members:
    - "human values and attitudes towards vaccination in social media"
    - "predicting demographics moral foundations and human values from digital behaviours"
    - "depechemood a bilingual emotion lexicon built through simple yet powerful techniques"
    - "automated hate speech detection and the problem of offensive language"
    - "quantifying moral foundations from various topics on twitter conversations"
    - "thumbs up or thumbs down semantic orientation applied to unsupervised classification of reviews"

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.HC(Human-Computer Interaction)