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 "20 years of mobility modeling prediction trends shortcomings perspectives"):
  Abstract: In this paper, we present a comprehensive survey of human-mobility modeling based on 1680 articles published between 1999 and 2019, which can serve as a roadmap for research and practice in this area. Mobility modeling research has accelerated the advancement of several fields of studies such as urban planning, epidemic modeling, traffic engineering and contributed to the development of location-based services. However, while the application of mobility models in different domains has increased, the credibility of the research results has decreased. We highlight two significant shortfalls commonly observed in our reviewed studies: (1) data-agnostic model selection resulting in a poor tradeoff between accuracy vs. complexity, and (2) failure to identify the source of empirical gains, due to adoption of inaccurate validation methodologies. We also observe troubling trends with respect to application of Markov model variants for modeling mobility, despite the questionable association of Markov processes and human-mobility dynamics. To this end, we propose a data-driven mobility-modeling framework that quantifies the characteristics of a dataset based on four mobility meta-attributes, in order to select the most appropriate prediction algorithm. Experimental evaluations on three real-world mobility datasets based on a rigorous validation methodology demonstrate our frameworks ability to correctly analyze the model accuracy vs. complexity tradeoff. We offer these results to the community along with the tools and the literature meta-data in order to improve the reliability and credibility of human mobility modeling research.

Includes members:
  Member "dilated recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "improving the gating mechanism of recurrent neural networks")
  Member "automatic keyword extraction for text summarization a survey":
    also connected through other hyperedges:
      (none)
  Member "federated multi task learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "think locally act globally federated learning with local and global representations")
  Member "hierarchical multiscale recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "refined gate a simple and effective gating mechanism for recurrent units")
  Member "cluster aided mobility predictions":
    also connected through other hyperedges:
      (none)

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)