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: "timeline generation tracking individuals on twitter"
  Abstract: In this paper, we propose a unsupervised framework to reconstruct a person's life history by creating a chronological list for {\it personal important events} (PIE) of individuals based on the tweets they published. By analyzing individual tweet collections, we find that what are suitable for inclusion in the personal timeline should be tweets talking about personal (as opposed to public) and time-specific (as opposed to time-general) topics. To further extract these types of topics, we introduce a non-parametric multi-level Dirichlet Process model to recognize four types of tweets: personal time-specific (PersonTS), personal time-general (PersonTG), public time-specific (PublicTS) and public time-general (PublicTG) topics, which, in turn, are used for further personal event extraction and timeline generation. To the best of our knowledge, this is the first work focused on the generation of timeline for individuals from twitter data. For evaluation, we have built a new golden standard Timelines based on Twitter and Wikipedia that contain PIE related events from 20 {\it ordinary twitter users} and 20 {\it celebrities}. Experiments on real Twitter data quantitatively demonstrate the effectiveness of our approach.

Connected through hyperedge HE-1 (induced by source paper "generating timelines by modeling semantic change"):
  with members:
    - "dynamic word embeddings for evolving semantic discovery"
    - "diachronic word embeddings and semantic shifts a survey"
    - "vector embedding of wikipedia concepts and entities"
    - "words are malleable computing semantic shifts in political and media discourse"
    - "diachronic word embeddings reveal statistical laws of semantic change"
    - "timemachine timeline generation for knowledge base entities"
    - "statistically significant detection of linguistic change"
    - "that s sick dude automatic identification of word sense change across different timescales"

Connected through hyperedge HE-2 (induced by source paper "a dynamic evolutionary framework for timeline generation based on distributed representations"):
  with members:
    - "growing story forest online from massive breaking news"
    - "socially informed timeline generation for complex events"
    - "enriching word vectors with subword information"
    - "siamese cbow optimizing word embeddings for sentence representations"
    - "timemachine timeline generation for knowledge base entities"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-3 (induced by source paper "detection and resolution of rumours in social media a survey"):
  with members:
    - "rumor detection and classification for twitter data"
    - "supporting the use of user generated content in journalistic practice"
    - "semeval 2017 task 8 rumoureval determining rumour veracity and support for rumours"
    - "simple open stance classification for rumour analysis"
    - "an efficient randomized algorithm for rumor blocking in online social networks"
    - "turing at semeval 2017 task 8 sequential approach to rumour stance classification with branch lstm"
    - "determining the veracity of rumours on twitter"
    - "hoaxy a platform for tracking online misinformation"

Connected through hyperedge HE-4 (induced by source paper "tracking the history and evolution of entities entity centric temporal analysis of large social media archives"):
  with members:
    - "semeval 2016 task 4 sentiment analysis in twitter"
    - "semeval 2017 task 4 sentiment analysis in twitter"
    - "tweetskb a public and large scale rdf corpus of annotated tweets"
    - "time aware and corpus specific entity relatedness"
    - "building and querying semantic layers for web archives extended version"
    - "balancing novelty and salience adaptive learning to rank entities for timeline summarization of high impact events"
    - "learning from the news predicting entity popularity on twitter"

Connected through hyperedge HE-5 (induced by source paper "timemachine timeline generation for knowledge base entities"):
  with members:
    - "learning mixtures of submodular shells with application to document summarization"

Connected through hyperedge HE-6 (induced by source paper "inferring user preferences by probabilistic logical reasoning over social networks"):
  with members:
    - "programming with personalized pagerank a locally groundable first order probabilistic logic"
    - "discriminative probabilistic models for relational data"
    - "probabilistic similarity logic"
    - "clp bn constraint logic programming for probabilistic knowledge"
    - "tuffy scaling up statistical inference in markov logic networks using an rdbms"

Connected through hyperedge HE-7 (induced by source paper "what a nasty day exploring mood weather relationship from twitter"):
  with members:
    - "early stage influenza detection from twitter"
    - "twitter mood predicts the stock market"
    - "measuring the happiness of large scale written expression songs blogs and presidents"

Connected through hyperedge HE-8 (induced by source paper "adaptive representations for tracking breaking news on twitter"):
  with members:
    - "time dependent hierarchical dirichlet model for timeline generation"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

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)