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: "multiview deep learning for predicting twitter users location"
  Abstract: The problem of predicting the location of users on large social networks like Twitter has emerged from real-life applications such as social unrest detection and online marketing. Twitter user geolocation is a difficult and active research topic with a vast literature. Most of the proposed methods follow either a content-based or a network-based approach. The former exploits user-generated content while the latter utilizes the connection or interaction between Twitter users. In this paper, we introduce a novel method combining the strength of both approaches. Concretely, we propose a multi-entry neural network architecture named MENET leveraging the advances in deep learning and multiview learning. The generalizability of MENET enables the integration of multiple data representations. In the context of Twitter user geolocation, we realize MENET with textual, network, and metadata features. Considering the natural distribution of Twitter users across the concerned geographical area, we subdivide the surface of the earth into multi-scale cells and train MENET with the labels of the cells. We show that our method outperforms the state of the art by a large margin on three benchmark datasets.

Connected through hyperedge HE-1 (induced by source paper "predictive analysis on twitter techniques and applications"):
  with members:
    - "what s my age predicting twitter user s age using influential friend network and dbpedia"
    - "on the challenges of sentiment analysis for dynamic events"
    - "early detection of promoted campaigns on social media"
    - "emojinet an open service and api for emoji sense discovery"
    - "location name extraction from targeted text streams using gazetteer based statistical language models"
    - "using social media to predict the future a systematic literature review"
    - "semi supervised approach to monitoring clinical depressive symptoms in social media"
    - "online human bot interactions detection estimation and characterization"

Connected through hyperedge HE-2 (induced by source paper "rumour detection via news propagation dynamics and user representation learning"):
  with members:
    - "all in one multi task learning for rumour verification"
    - "fakedetector effective fake news detection with deep diffusive neural network"
    - "detection and resolution of rumours in social media a survey"
    - "ti cnn convolutional neural networks for fake news detection"
    - "liar liar pants on fire a new benchmark dataset for fake news detection"
    - "call attention to rumors deep attention based recurrent neural networks for early rumor detection"
    - "a stylometric inquiry into hyperpartisan and fake news"
    - "fake news detection on social media a data mining perspective"

Connected through hyperedge HE-3 (induced by source paper "location reference identification from tweets during emergencies a deep learning approach"):
  with members:
    - "a survey of location prediction on twitter"
    - "savitr a system for real time location extraction from microblogs during emergencies"
    - "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-4 (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"
    - "on predicting geolocation of tweets using convolutional neural networks"
    - "attention is all you need"
    - "a neural model for user geolocation and lexical dialectology"
    - "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-5 (induced by source paper "a practical guide for the effective evaluation of twitter user geolocation"):
  with members:
    - "a survey of location prediction on twitter"
    - "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"
    - "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-6 (induced by source paper "city level geolocation of tweets for real time visual analytics"):
  with members:
    - "interactive learning for identifying relevant tweets to support real time situational awareness"
    - "location reference identification from tweets during emergencies a deep learning approach"
    - "end to end network for twitter geolocation prediction and hashing"
    - "a probabilistic framework for location inference from social media"
    - "convolutional neural networks for sentence classification"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-7 (induced by source paper "semi supervised user geolocation via graph convolutional networks"):
  with members:
    - "continuous representation of location for geolocation and lexical dialectology using mixture density networks"
    - "a neural model for user geolocation and lexical dialectology"
    - "density estimation for geolocation via convolutional mixture density network"
    - "exploiting text and network context for geolocation of social media users"
    - "geotagging one hundred million twitter accounts with total variation minimization"
    - "is the sample good enough comparing data from twitter s streaming api with twitter s firehose"

Connected through hyperedge HE-8 (induced by source paper "geocoding without geotags a text based approach for reddit"):
  with members:
    - "a neural model for user geolocation and lexical dialectology"
    - "exploiting text and network context for geolocation of social media users"
    - "a survey of location inference techniques on twitter"
    - "evolution of reddit from the front page of the internet to a self referential community"

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.LG(Machine Learning)