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: "online reputation and polling systems data incest social learning and revealed preferences"
  Abstract: This paper considers online reputation and polling systems where individuals make recommendations based on their private observations and recommendations of friends. Such interaction of individuals and their social influence is modelled as social learning on a directed acyclic graph. Data incest (misinformation propagation) occurs due to unintentional re-use of identical actions in the for- mation of public belief in social learning; the information gathered by each agent is mistakenly considered to be independent. This results in overconfidence and bias in estimates of the state. Necessary and sufficient conditions are given on the structure of information exchange graph to mitigate data incest. Incest removal algorithms are presented. Experimental results on human subjects are presented to illustrate the effect of social influence and data incest on decision making. These experimental results indicate that social learning protocols require careful design to handle and mitigate data incest. The incest removal algorithms are illustrated in an expectation polling system where participants in a poll respond with a summary of their friends' beliefs. Finally, the principle of revealed preferences arising in micro-economics theory is used to parse Twitter datasets to determine if social sensors are utility maximizers and then determine their utility functions.

Connected through hyperedge HE-1 (induced by source paper "what do your friends think efficient polling methods for networks using friendship paradox"):
  with members:
    - "information diffusion in social networks friendship paradox based models and statistical inference"
    - "friendship paradox biases perceptions in directed networks"
    - "centrality friendship paradoxes when our friends are more important than us"
    - "gemsec graph embedding with self clustering"
    - "the majority illusion in social networks"
    - "qualities and inequalities in online social networks through the lens of the generalized friendship paradox"
    - "generalized friendship paradox in complex networks the case of scientific collaboration"
    - "friendship paradox redux your friends are more interesting than you"

Connected through hyperedge HE-2 (induced by source paper "adaptive polling in hierarchical social networks using blackwell dominance"):
  with members:
    - "what do your friends think efficient polling methods for networks using friendship paradox"
    - "coarse graining and the blackwell order"
    - "140 characters to victory using twitter to predict the uk 2015 general election"
    - "active sequential hypothesis testing"
    - "human as real time sensors of social and physical events a case study of twitter and sports games"

Connected through hyperedge HE-3 (induced by source paper "pomdp structural results for controlled sensing"):
  with members:
    - "tracking infection diffusion in social networks filtering algorithms and threshold bounds"

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)