human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "effectiveness of alter sampling in social networks"):
  Abstract: Social networks play a key role in studying various individual and social behaviors. To use social networks in a study, their structural properties must be measured. For offline social networks, the conventional procedure is surveying/interviewing a set of randomly-selected respondents. In many practical applications, inferring the network structure via sampling is too prohibitively costly. There are also applications in which it simply fails. For example, for optimal vaccination or employing influential spreaders for public health interventions, we need to efficiently and quickly target well-connected individuals, which random sampling does not accomplish. In a few studies, an alternative sampling scheme (which we dub `alter sampling') has proven useful. This method simply targets randomly-chosen neighbors of the randomly-selected respondents. A natural question that arises is: to what extent does this method generalize? Is the method suitable for every social network or only the very few ones considered so far? In this paper, we demonstrate the robustness of this method across a wide range of networks with diverse structural properties. The method outperforms random sampling by a large margin for a vast majority of cases. We then propose an estimator to assess the advantage of choosing alter sampling over random sampling in practical scenarios, and demonstrate its accuracy via Monte Carlo simulations on diverse synthetic networks.

Includes members:
  Member "using friends as sensors to detect global scale contagious outbreaks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "information diffusion in social networks friendship paradox based models and statistical inference")
  Member "defining and evaluating network communities based on ground truth":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "certified robustness of community detection against adversarial structural perturbation via randomized smoothing")
  Member "information contagion an empirical study of the spread of news on digg and twitter social networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "modeling information diffusion in online social networks with partial differential equations")
  Member "community structure in large networks natural cluster sizes and the absence of large well defined clusters":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "certified robustness of community detection against adversarial structural perturbation via randomized smoothing")

Question: Please tell me which class the source paper 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)