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 large scale empirical study of geotagging behavior on twitter"):
  Abstract: Geotagging on social media has become an important proxy for understanding people's mobility and social events. Research that uses geotags to infer public opinions relies on several key assumptions about the behavior of geotagged and non-geotagged users. However, these assumptions have not been fully validated. Lack of understanding the geotagging behavior prohibits people further utilizing it. In this paper, we present an empirical study of geotagging behavior on Twitter based on more than 40 billion tweets collected from 20 million users. There are three main findings that may challenge these common assumptions. Firstly, different groups of users have different geotagging preferences. For example, less than 3% of users speaking in Korean are geotagged, while more than 40% of users speaking in Indonesian use geotags. Secondly, users who report their locations in profiles are more likely to use geotags, which may affects the generability of those location prediction systems on non-geotagged users. Thirdly, strong homophily effect exists in users' geotagging behavior, that users tend to connect to friends with similar geotagging preferences.

Includes members:
  Member "parameterized convolutional neural networks for aspect level sentiment classification":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "hybrid tiled convolutional neural networks for text sentiment classification")
  Member "location order recovery in trails with low temporal resolution":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a hierarchical location prediction neural network for twitter user geolocation")
  Member "aspect level sentiment classification with attention over attention neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "discovering protagonist of sentiment with aspect reconstructed capsule network")
  Member "on predicting geolocation of tweets using convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "discover your social identity from what you tweet a content based approach")
  Member "a probabilistic framework for location inference from social media":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "location reference identification from tweets during emergencies a deep learning approach")
  Member "twitter user geolocation using a unified text and network prediction model":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a hierarchical location prediction neural network for twitter user geolocation")
  Member "geo located twitter as proxy for global mobility patterns":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "transfer learning from an auxiliary discriminative task for unsupervised anomaly detection")
  Member "where in the world are you geolocation and language identification in twitter":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a hierarchical location prediction neural network for twitter user geolocation")

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)