human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "location reference identification from tweets during emergencies a deep learning approach"):
  with members:
    - "savitr a system for real time location extraction from microblogs during emergencies"
    - "multiview deep learning for predicting twitter users location"
    - "a probabilistic framework for location inference from social media"
    - "location name extraction from targeted text streams using gazetteer based statistical language models"
    - "rapid classification of crisis related data on social networks using convolutional neural networks"
    - "neural architectures for named entity recognition"
    - "bidirectional lstm crf models for sequence tagging"
    - "named entity recognition with bidirectional lstm cnns"

Connected through hyperedge HE-2 (induced by source paper "please forget where i was last summer the privacy risks of public location meta data"):
  with members:
    - "the long road to computational location privacy a survey"
    - "you are your metadata identification and obfuscation of social media users using metadata information"
    - "knock knock who s there membership inference on aggregate location data"
    - "trajectory recovery from ash user privacy is not preserved in aggregated mobility data"
    - "what does the crowd say about you evaluating aggregation based location privacy"
    - "walk2friends inferring social links from mobility profiles"
    - "demo recon revealing and controlling pii leaks in mobile network traffic"
    - "explore spatiotemporal and demographic characteristics of human mobility via twitter a case study of chicago"

Connected through hyperedge HE-3 (induced by source paper "a practical guide for the effective evaluation of twitter user geolocation"):
  with members:
    - "semi supervised user geolocation via graph convolutional networks"
    - "replicability analysis for natural language processing testing significance with multiple datasets"
    - "continuous representation of location for geolocation and lexical dialectology using mixture density networks"
    - "multiview deep learning for predicting twitter users location"
    - "confounds and consequences in geotagged twitter data"
    - "performance of social network sensors during hurricane sandy"
    - "inferring the origin locations of tweets with quantitative confidence"
    - "machine learning in automated text categorization"

Connected through hyperedge HE-4 (induced by source paper "dlocrl a deep learning pipeline for fine grained location recognition and linking in tweets"):
  with members:
    - "deep contextualized word representations"
    - "disan directional self attention network for rnn cnn free language understanding"
    - "multi task cross lingual sequence tagging from scratch"
    - "bidirectional lstm crf models for sequence tagging"
    - "transition based dependency parsing with stack long short term memory"
    - "named entity recognition with bidirectional lstm cnns"
    - "natural language processing almost from scratch"

Connected through hyperedge HE-5 (induced by source paper "towards better social crisis data with hermes hybrid sensing for emergency management system"):
  with members:
    - "a practitioners guide to transfer learning for text classification using convolutional neural networks"
    - "rapid classification of crisis related data on social networks using convolutional neural networks"
    - "twitter as a lifeline human annotated twitter corpora for nlp of crisis related messages"
    - "processing social media messages in mass emergency a survey"
    - "distributed representations of words and phrases and their compositionality"
    - "natural language processing almost from scratch"
    - "fast and accurate annotation of short texts with wikipedia pages"

Connected through hyperedge HE-6 (induced by source paper "exploiting user frequency information for mining regionalisms from social media texts"):
  with members:
    - "continuous representation of location for geolocation and lexical dialectology using mixture density networks"
    - "a neural model for user geolocation and lexical dialectology"
    - "diffusion of lexical change in social media"
    - "entropy based classification of retweeting activity on twitter"

Connected through hyperedge HE-7 (induced by source paper "high resolution home location prediction from tweets using deep learning with dynamic structure"):
  with members:
    - "a survey of location inference techniques on twitter"
    - "home location identification of twitter users"

Connected through hyperedge HE-8 (induced by source paper "infringement of tweets geo location privacy an approach based on graph convolutional neural networks"):
  with members:
    - "where in the world are you geolocation and language identification in twitter"
    - "a convolutional neural network for modelling sentences"

Question: Please tell me which class the center node 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)