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: "on predicting geolocation of tweets using convolutional neural networks"
  Abstract: In many Twitter studies, it is important to know where a tweet came from in order to use the tweet content to study regional user behavior. However, researchers using Twitter to understand user behavior often lack sufficient geo-tagged data. Given the huge volume of Twitter data there is a need for accurate automated geolocating solutions. Herein, we present a new method to predict a Twitter user’s location based on the information in a single tweet. We integrate text and user profile meta-data into a single model using a convolutional neural network. Our experiments demonstrate that our neural model substantially outperforms baseline methods, achieving 52.8% accuracy and 92.1% accuracy on city-level and country-level prediction respectively.

Connected through hyperedge HE-1 (induced by source paper "discover your social identity from what you tweet a content based approach"):
  with members:
    - "detecting gan generated fake images using co occurrence matrices"
    - "exploiting multi domain visual information for fake news detection"
    - "a hierarchical location prediction neural network for twitter user geolocation"
    - "fact checking meets fauxtography verifying claims about images"
    - "false news detection on social media"
    - "aird adversarial learning framework for image repurposing detection"
    - "fakenewsnet a data repository with news content social context and dynamic information for studying fake news on social media"
    - "exposing deep fakes using inconsistent head poses"

Connected through hyperedge HE-2 (induced by source paper "a hierarchical location prediction neural network for twitter user geolocation"):
  with members:
    - "location order recovery in trails with low temporal resolution"
    - "semi supervised user geolocation via graph convolutional networks"
    - "attention is all you need"
    - "a neural model for user geolocation and lexical dialectology"
    - "multiview deep learning for predicting twitter users location"
    - "a probabilistic framework for location inference from social media"
    - "inferring the location of authors from words in their texts"
    - "exploiting text and network context for geolocation of social media users"

Connected through hyperedge HE-3 (induced by source paper "parameterized convolutional neural networks for aspect level sentiment classification"):
  with members:
    - "semeval 2017 task 4 sentiment analysis in twitter"
    - "aspect level sentiment classification with attention over attention neural networks"
    - "learning to attend via word aspect associative fusion for aspect based sentiment analysis"
    - "interactive attention networks for aspect level sentiment classification"
    - "aspect level sentiment classification with deep memory network"
    - "language modeling with gated convolutional networks"
    - "end to end memory networks"
    - "effective lstms for target dependent sentiment classification"

Connected through hyperedge HE-4 (induced by source paper "a large scale empirical study of geotagging behavior on twitter"):
  with members:
    - "parameterized convolutional neural networks for aspect level sentiment classification"
    - "location order recovery in trails with low temporal resolution"
    - "aspect level sentiment classification with attention over attention neural networks"
    - "a probabilistic framework for location inference from social media"
    - "twitter user geolocation using a unified text and network prediction model"
    - "geo located twitter as proxy for global mobility patterns"
    - "where in the world are you geolocation and language identification in twitter"

Connected through hyperedge HE-5 (induced by source paper "location order recovery in trails with low temporal resolution"):
  with members:
    - "a probabilistic framework for location inference from social media"
    - "subspace pursuit for compressive sensing signal reconstruction"

Connected through hyperedge HE-6 (induced by source paper "aspect level sentiment classification with attention over attention neural networks"):
  with members:
    - "attention over attention neural networks for reading comprehension"
    - "interactive attention networks for aspect level sentiment classification"
    - "aspect level sentiment classification with deep memory network"
    - "effective lstms for target dependent sentiment classification"
    - "adam a method for stochastic optimization"
    - "convolutional neural networks for sentence classification"
    - "thumbs up sentiment classification using machine learning techniques"

Connected through hyperedge HE-7 (induced by source paper "social media news and political information during the us election was polarizing content concentrated in swing states"):
  with members:
    - "a survey of location inference techniques on twitter"

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)