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 hierarchical location prediction neural network for twitter user geolocation"):
  Abstract: Accurate estimation of user location is important for many online services. Previous neural network based methods largely ignore the hierarchical structure among locations. In this paper, we propose a hierarchical location prediction neural network for Twitter user geolocation. Our model first predicts the home country for a user, then uses the country result to guide the city-level prediction. In addition, we employ a character-aware word embedding layer to overcome the noisy information in tweets. With the feature fusion layer, our model can accommodate various feature combinations and achieves state-of-the-art results over three commonly used benchmarks under different feature settings. It not only improves the prediction accuracy but also greatly reduces the mean error distance.

Includes members:
  Member "location order recovery in trails with low temporal resolution":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a large scale empirical study of geotagging behavior on twitter")
  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 "on predicting geolocation of tweets using convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "discover your social identity from what you tweet a content based approach")
  Member "attention is all you need":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "a neural model for user geolocation and lexical dialectology":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "automatic location type classification from social media posts")
  Member "multiview deep learning for predicting twitter users location":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "predictive analysis on twitter techniques and applications")
  Member "a probabilistic framework for location inference from social media":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "location reference identification from tweets during emergencies a deep learning approach")
  Member "inferring the location of authors from words in their texts":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "on predicting geolocation of tweets using convolutional neural networks")

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)