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: "quantified self meets social media sharing of weight updates on twitter"
  Abstract: An increasing number of people use wearables and other smart devices to quantify various health conditions, ranging from sleep patterns, to body weight, to heart rates. Of these Quantified Selfs many choose to openly share their data via online social networks such as Twitter and Facebook. In this study, we use data for users who have chosen to connect their smart scales to Twitter, providing both a reliable time series of their body weight, as well as insights into their social surroundings and general online behavior. Concretely, we look at which social media features are predictive of physical status, such as body weight at the individual level, and activity patterns at the population level. We show that it is possible to predict an individual's weight using their online social behaviors, such as their self-description and tweets. Weekly and monthly patterns of quantified-self behaviors are also discovered. These findings could contribute to building models to monitor public health and to have more customized personal training interventions. #R##N#While there are many studies using either quantified self or social media data in isolation, this is one of the few that combines the two data sources and, to the best of our knowledge, the only one that uses public data.

Connected through hyperedge HE-1 (induced by source paper "mining social media data for biomedical signals and health related behavior"):
  with members:
    - "singular value decomposition and neural networks"
    - "anatomy of an online misinformation network"
    - "human sexual cycles are driven by culture and match collective moods"
    - "limited individual attention and online virality of low quality information"
    - "the rise of social bots"
    - "learning deep structure preserving image text embeddings"
    - "insights from machine learned diet success prediction"
    - "benchmarking sentiment analysis methods for large scale texts a case for using continuum scored words and word shift graphs"

Connected through hyperedge HE-2 (induced by source paper "eat tell a randomized trial of random loss incentive to increase dietary self tracking compliance"):
  with members:
    - "foodporn obesity patterns in culinary interactions"
    - "you tweet what you eat studying food consumption through twitter"

Connected through hyperedge HE-3 (induced by source paper "hearts and politics metrics for tracking biorhythm changes during brexit and trump"):
  with members:
    - "how gamification affects physical activity large scale analysis of walking challenges in a mobile application"

Connected through hyperedge HE-4 (induced by source paper "a warm welcome matters the link between social feedback and weight loss in r loseit"):
  with members:
    - "the effect of social feedback in a reddit weight loss community"
    - "persistent sharing of fitness app status on twitter"
    - "investigating causality in human behavior from smartphone sensor data a quasi experimental approach"
    - "how community feedback shapes user behavior"

Connected through hyperedge HE-5 (induced by source paper "crowdsourcing health labels inferring body weight from profile pictures"):
  with members:
    - "you tweet what you eat studying food consumption through 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)