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: "keep your friends close and your facebook friends closer a multiplex network approach to the analysis of offline and online social ties"
  Abstract: Social media allow for an unprecedented amount of interaction between people online. A fundamental aspect of human social behavior, however, is the tendency of people to associate themselves with like-minded individuals, forming homogeneous social circles both online and offline. In this work, we apply a new model that allows us to distinguish between social ties of varying strength, and to observe evidence of homophily with regards to politics, music, health, residential sector & year in college, within the online and offline social network of 74 college students. We present a multiplex network approach to social tie strength, here applied to mobile communication data - calls, text messages, and co-location, allowing us to dimensionally identify relationships by considering the number of communication channels utilized between students. We find that strong social ties are characterized by maximal use of communication channels, while weak ties by minimal use. We are able to identify 75% of close friendships, 90% of weaker ties, and 90% of Facebook friendships as compared to reported ground truth. We then show that stronger ties exhibit greater profile similarity than weaker ones. Apart from high homogeneity in social circles with respect to political and health aspects, we observe strong homophily driven by music, residential sector and year in college. Despite Facebook friendship being highly dependent on residence and year, exposure to less homogeneous content can be found in the online rather than the offline social circles of students, most notably in political and music aspects.

Connected through hyperedge HE-1 (induced by source paper "sensing and modeling human behavior using social media and mobile data"):
  with members:
    - "a multilayer approach to multiplexity and link prediction in online geo social networks"
    - "anticipatory mobile computing a survey of the state of the art and research challenges"
    - "investigating causality in human behavior from smartphone sensor data a quasi experimental approach"
    - "you are what you eat and drink identifying cultural boundaries by analyzing food drink habits in foursquare"
    - "hoodsquare modeling and recommending neighborhoods in location based social networks"
    - "predicting influential users in online social networks"

Connected through hyperedge HE-2 (induced by source paper "when engagement meets similarity efficient k r core computation on social networks"):
  with members:
    - "i o efficient core graph decomposition at web scale"
    - "towards realistic team formation in social networks based on densest subgraphs"
    - "locally estimating core numbers"
    - "geo social group queries with minimum acquaintance constraint"
    - "preventing unraveling in social networks gets harder"
    - "parameterized complexity of the anchored k core problem for directed graphs"
    - "community detection in networks with node attributes"
    - "mining attribute structure correlated patterns in large attributed graphs"

Connected through hyperedge HE-3 (induced by source paper "the international postal network and other global flows as proxies for national wellbeing"):
  with members:
    - "big questions for social media big data representativeness validity and other methodological pitfalls"
    - "a degree centrality in multi layered social network"
    - "the anatomy of the facebook social graph"
    - "dimes let the internet measure itself"

Connected through hyperedge HE-4 (induced by source paper "a multilayer approach to multiplexity and link prediction in online geo social networks"):
  with members:
    - "measuring large scale social networks with high resolution"
    - "social bootstrapping how pinterest and last fm social communities benefit by borrowing links from facebook"
    - "multi layered social networks"
    - "supervised random walks predicting and recommending links in social networks"

Connected through hyperedge HE-5 (induced by source paper "on the k anonymization of time varying and multi layer social graphs"):
  with members:
    - "improved upper and lower bound heuristics for degree anonymization in social networks"

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)