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 "towards a standard sampling methodology on online social networks collecting global trends on twitter"):
  Abstract: One of the most significant current challenges in large-scale online social networks, is to establish a concise and coherent method able to collect and summarize data. Sampling the content of an Online Social Network (OSN) plays an important role as a knowledge discovery tool. #R##N#It is becoming increasingly difficult to ignore the fact that current sampling methods must cope with a lack of a full sampling frame i.e., there is an imposed condition determined by a limited data access. In addition, another key aspect to take into account is the huge amount of data generated by users of social networking services. This type of conditions make especially difficult to develop sampling methods to collect truly reliable data. Therefore, we propose a low computational cost method for sampling emerging global trends on social networking services such as Twitter. #R##N#The main purpose of this study, is to develop a methodology able to carry out an efficient collecting process via three random generators: Brownian, Illusion and Reservoir. These random generators will be combined with a Metropolis-Hastings Random Walk (MHRW) in order to improve the sampling process. We demonstrate the effectiveness of our approach by correctly providing a descriptive statistics of the collected data. In addition, we also sketch the collecting procedure on real-time carried out on Twitter. Finally, we conclude with a trend concentration graphical description and a formal convergence analysis to evaluate whether the sample of draws has attained an equilibrium state to get a rough estimate of the sample quality.

Includes members:
  Member "geo located twitter as proxy for global mobility patterns":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "transfer learning from an auxiliary discriminative task for unsupervised anomaly detection")
  Member "predicting crowd behavior with big public data":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "mining social media data for biomedical signals and health related behavior")
  Member "bigbirds never die understanding social dynamics of emergent hashtag":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "language in our time an empirical analysis of hashtags")
  Member "the role of information diffusion in the evolution of social networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on the inevitability of online echo chambers")
  Member "organization mining using online social networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "online social networks threats and solutions")
  Member "virality prediction and community structure in social networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep collaborative embedding for information cascade prediction")
  Member "the anatomy of the facebook social graph":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "graph bert only attention is needed for learning graph representations")
  Member "supervised random walks predicting and recommending links in social networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "few shot learning on graphs via super classes based on graph spectral measures")

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)