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: "experience versus talent shapes the structure of the web"
  Abstract: We use sequential large-scale crawl data to empirically investigate and validate the dynamics that underlie the evolution of the structure of the web. We find that the overall structure of the web is defined by an intricate interplay between experience or entitlement of the pages (as measured by the number of inbound hyperlinks a page already has), inherent talent or fitness of the pages (as measured by the likelihood that someone visiting the page would give a hyperlink to it), and the continual high rates of birth and death of pages on the web. We find that the web is conservative in judging talent and the overall fitness distribution is exponential, showing low variability. The small variance in talent, however, is enough to lead to experience distributions with high variance: The preferential attachment mechanism amplifies these small biases and leads to heavy-tailed power-law (PL) inbound degree distributions over all pages, as well as over pages that are of the same age. The balancing act between experience and talent on the web allows newly introduced pages with novel and interesting content to grow quickly and surpass older pages. In this regard, it is much like what we observe in high-mobility and meritocratic societies: People with entitlement continue to have access to the best resources, but there is just enough screening for fitness that allows for talented winners to emerge and join the ranks of the leaders. Finally, we show that the fitness estimates have potential practical applications in ranking query results.

Connected through hyperedge HE-1 (induced by source paper "the evolutions of the rich get richer and the fit get richer phenomena in scholarly networks the case of the strategic management journal"):
  with members:
    - "the scaling relationship between citation based performance and coauthorship patterns in natural sciences"
    - "quantifying long term scientific impact"
    - "betweenness centrality as a driver of preferential attachment in the evolution of research collaboration networks"
    - "cwts crown indicator measures citation impact of a research group s publication oeuvre"
    - "towards a new crown indicator an empirical analysis"

Connected through hyperedge HE-2 (induced by source paper "network structure exploration via bayesian nonparametric models"):
  with members:
    - "overlapping community detection in networks with positive and negative links"
    - "generalized friendship paradox in complex networks the case of scientific collaboration"
    - "finding missing edges in networks based on their community structure"
    - "nonparametric link prediction in dynamic networks"
    - "an infinite latent attribute model for network data"
    - "efficient modularity optimization by multistep greedy algorithm and vertex mover refinement"

Connected through hyperedge HE-3 (induced by source paper "generalized friendship paradox in complex networks the case of scientific collaboration"):
  with members:
    - "friendship paradox redux your friends are more interesting than you"
    - "virality prediction and community structure in social networks"
    - "using friends as sensors to detect global scale contagious outbreaks"
    - "the role of social networks in information diffusion"
    - "the anatomy of the facebook social graph"

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.CY(Computers and Society)