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: "the happiness paradox your friends are happier than you"
  Abstract: Most individuals in social networks experience a so-called Friendship Paradox: they are less popular than their friends on average. This effect may explain recent findings that widespread social network media use leads to reduced happiness. However the relation between popularity and happiness is poorly understood. A Friendship paradox does not necessarily imply a Happiness paradox where most individuals are less happy than their friends. Here we report the first direct observation of a significant Happiness Paradox in a large-scale online social network of $39,110$ Twitter users. Our results reveal that popular individuals are indeed happier and that a majority of individuals experience a significant Happiness paradox. The magnitude of the latter effect is shaped by complex interactions between individual popularity, happiness, and the fact that users cluster assortatively by level of happiness. Our results indicate that the topology of online social networks and the distribution of happiness in some populations can cause widespread psycho-social effects that affect the well-being of billions of individuals.

Connected through hyperedge HE-1 (induced by source paper "information diffusion in social networks friendship paradox based models and statistical inference"):
  with members:
    - "what do your friends think efficient polling methods for networks using friendship paradox"
    - "centrality friendship paradoxes when our friends are more important than us"
    - "conjoining uncooperative societies facilitates evolution of cooperation"
    - "contagions in social networks effects of monophilic contagion friendship paradox and reactive networks"
    - "which friends are more popular than you contact strength and the friendship paradox in social networks"
    - "influence maximization over markovian graphs a stochastic optimization approach"
    - "engagement dynamics and sensitivity analysis of youtube videos"
    - "the majority illusion in social networks"

Connected through hyperedge HE-2 (induced by source paper "modeling spatiotemporal factors associated with sentiment on twitter synthesis and suggestions for improving the identification of localized deviations"):
  with members:
    - "a survey of location inference techniques on twitter"
    - "what a nasty day exploring mood weather relationship from twitter"
    - "home location identification of twitter users"
    - "geotagging one hundred million twitter accounts with total variation minimization"
    - "comparing and combining sentiment analysis methods"
    - "the dynamics of health behavior sentiments on a large online social network"
    - "assessing vaccination sentiments with online social media implications for infectious disease dynamics and control"
    - "twitter mood predicts the stock market"

Connected through hyperedge HE-3 (induced by source paper "contagions in social networks effects of monophilic contagion friendship paradox and reactive networks"):
  with members:
    - "what do your friends think efficient polling methods for networks using friendship paradox"
    - "centrality friendship paradoxes when our friends are more important than us"
    - "which friends are more popular than you contact strength and the friendship paradox in social networks"
    - "influence maximization over markovian graphs a stochastic optimization approach"
    - "tracking infection diffusion in social networks filtering algorithms and threshold bounds"
    - "scalable methods for adaptively seeding a social network"
    - "friendship paradox redux your friends are more interesting than you"
    - "using friends as sensors to detect global scale contagious outbreaks"

Connected through hyperedge HE-4 (induced by source paper "centrality friendship paradoxes when our friends are more important than us"):
  with members:
    - "network weirdness exploring the origins of network paradoxes"
    - "generalized friendship paradox in complex networks the case of scientific collaboration"
    - "friendship paradox redux your friends are more interesting than you"
    - "total communicability as a centrality measure"
    - "using friends as sensors to detect global scale contagious outbreaks"

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)