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 "reciprocal versus parasocial relationships in online social networks"):
  Abstract: Many online social networks are fundamentally directed, i.e., they consist of both reciprocal edges (i.e., edges that have already been linked back) and parasocial edges (i.e., edges that haven't been linked back). Thus, understanding the structures and evolutions of reciprocal edges and parasocial ones, exploring the factors that influence parasocial edges to become reciprocal ones, and predicting whether a parasocial edge will turn into a reciprocal one are basic research problems. #R##N#However, there have been few systematic studies about such problems. In this paper, we bridge this gap using a novel large-scale Google+ dataset crawled by ourselves as well as one publicly available social network dataset. First, we compare the structures and evolutions of reciprocal edges and those of parasocial edges. For instance, we find that reciprocal edges are more likely to connect users with similar degrees while parasocial edges are more likely to link ordinary users (e.g., users with low degrees) and popular users (e.g., celebrities). However, the impacts of reciprocal edges linking ordinary and popular users on the network structures increase slowly as the social networks evolve. Second, we observe that factors including user behaviors, node attributes, and edge attributes all have significant impacts on the formation of reciprocal edges. Third, in contrast to previous studies that treat reciprocal edge prediction as either a supervised or a semi-supervised learning problem, we identify that reciprocal edge prediction is better modeled as an outlier detection problem. Finally, we perform extensive evaluations with the two datasets, and we show that our proposal outperforms previous reciprocal edge prediction approaches.

Includes members:
  Member "dynamics of trust reciprocation in heterogenous mmog networks":
    also connected through other hyperedges:
      (none)
  Member "directed closure measures for networks with reciprocity":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "measuring directed triadic closure with closure coefficients")
  Member "multi scale dynamics in a massive online social network":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on the fundamental equation of user dynamics and the structure of online social networks")
  Member "a scalable null model for directed graphs matching all degree distributions in out and reciprocal":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "recent advances in scalable network generation")
  Member "nonparametric link prediction in dynamic networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "dyngraph2vec capturing network dynamics using dynamic graph representation learning")
  Member "evolution of social attribute networks measurements modeling and implications using google":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "defending against machine learning based inference attacks via adversarial examples opportunities and challenges")
  Member "predicting links and inferring attributes using a social attribute network san":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "clustering attributed graphs models measures and methods")
  Member "four degrees of separation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "diffusion improves graph learning")

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)