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 "analyzing complex network user arrival patterns and their effect on network topologies"):
  Abstract: Complex networks have non-trivial characteristics and appear in many real-world systems. Due to their vital importance in a large number of research fields, various studies have offered explanations on how complex networks evolve, but the full underlying dynamics of complex networks are not completely understood. Many of the barriers to better understanding the evolution process of these networks can be removed with the emergence of new data sources. #R##N#This study utilizes the recently published Reddit dataset, containing over 1.65 billion comments, to construct the largest publicly available social network corpus, which contains detailed information on the evolution process of 11,965 social networks. We used this dataset to study the effect of the patterns in which new users join a network (referred to as user arrival curves, or UACs) on the network topology. Our results present evidence that UACs are a central factor in molding a network's topology; that is, different arrival patterns create different topological properties. Additionally, we show that it is possible to uncover the types of user arrival patterns by analyzing a social network's topology. These results imply that existing complex network evolution models need to be revisited and modified to include user arrival patterns as input to the models, in order to create models that more accurately reflect real-world complex networks.

Includes members:
  Member "deep neural network based malware detection using two dimensional binary program features":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "automatic generation of adversarial examples for interpreting malware classifiers")
  Member "evolution of social attribute networks measurements modeling and implications using google":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "defending against machine learning based inference attacks via adversarial examples opportunities and challenges")
  Member "graphlab a new framework for parallel machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "relaxed scheduling for scalable belief propagation")

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)