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 survey of location prediction on twitter"):
  Abstract: Locations, e.g., countries, states, cities, and point-of-interests, are central to news, emergency events, and people's daily lives. Automatic identification of locations associated with or mentioned in documents has been explored for decades. As one of the most popular online social network platforms, Twitter has attracted a large number of users who send millions of tweets on daily basis. Due to the world-wide coverage of its users and real-time freshness of tweets, location prediction on Twitter has gained significant attention in recent years. Research efforts are spent on dealing with new challenges and opportunities brought by the noisy, short, and context-rich nature of tweets. In this survey, we aim at offering an overall picture of location prediction on Twitter. Specifically, we concentrate on the prediction of user home locations, tweet locations, and mentioned locations. We first define the three tasks and review the evaluation metrics. By summarizing Twitter network, tweet content, and tweet context as potential inputs, we then structurally highlight how the problems depend on these inputs. Each dependency is illustrated by a comprehensive review of the corresponding strategies adopted in state-of-the-art approaches. In addition, we also briefly review two related problems, i.e., semantic location prediction and point-of-interest recommendation. Finally, we make a conclusion of the survey and list future research directions.

Includes members:
  Member "continuous representation of location for geolocation and lexical dialectology using mixture density networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a practical guide for the effective evaluation of twitter user geolocation")
  Member "a neural model for user geolocation and lexical dialectology":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "automatic location type classification from social media posts")
  Member "density estimation for geolocation via convolutional mixture density network":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a hierarchical mixture density network")
  Member "a survey of point of interest recommendation in location based social networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "practical privacy preserving poi recommendation")
  Member "exploiting text and network context for geolocation of social media users":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a hierarchical location prediction neural network for twitter user geolocation")
  Member "analysis of named entity recognition and linking for tweets":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "benchmarking knowledge graphs on the web")
  Member "twitter user geolocation using a unified text and network prediction model":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a hierarchical location prediction neural network for twitter user geolocation")
  Member "a survey of location inference techniques on twitter":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "location reference identification from tweets during emergencies a deep learning approach")

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)