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 "a practical guide for the effective evaluation of twitter user geolocation"):
  Abstract: Geolocating Twitter users---the task of identifying their home locations---serves a wide range of community and business applications such as managing natural crises, journalism, and public health. Many approaches have been proposed for automatically geolocating users based on their tweets; at the same time, various evaluation metrics have been proposed to measure the effectiveness of these approaches, making it challenging to understand which of these metrics is the most suitable for this task. In this paper, we propose a guide for a standardized evaluation of Twitter user geolocation by analyzing fifteen models and two baselines in a controlled experimental setting. Models are evaluated using ten metrics over four geographic granularities. We use rank correlations to assess the effectiveness of these metrics. #R##N#Our results demonstrate that the choice of effectiveness metric can have a substantial impact on the conclusions drawn from a geolocation system experiment, potentially leading experimenters to contradictory results about relative effectiveness. We show that for general evaluations, a range of performance metrics should be reported, to ensure that a complete picture of system effectiveness is conveyed. Given the global geographic coverage of this task, we specifically recommend evaluation at micro versus macro levels to measure the impact of the bias in distribution over locations. Although a lot of complex geolocation algorithms have been applied in recent years, a majority class baseline is still competitive at coarse geographic granularity. We propose a suite of statistical analysis tests, based on the employed metric, to ensure that the results are not coincidental.

Includes members:
  Member "a survey of location prediction on twitter":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "location reference identification from tweets during emergencies a deep learning approach")
  Member "semi supervised user geolocation via graph convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deepgcns making gcns go as deep as cnns")
  Member "replicability analysis for natural language processing testing significance with multiple datasets":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "show your work improved reporting of experimental results")
  Member "continuous representation of location for geolocation and lexical dialectology using mixture density networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "exploiting user frequency information for mining regionalisms from social media texts")
  Member "multiview deep learning for predicting twitter users location":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "predictive analysis on twitter techniques and applications")
  Member "confounds and consequences in geotagged twitter data":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a total error framework for digital traces of humans")
  Member "performance of social network sensors during hurricane sandy":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "mining social media data for biomedical signals and health related behavior")
  Member "inferring the origin locations of tweets with quantitative confidence":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a survey of location prediction on twitter")

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)