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: "timemachine timeline generation for knowledge base entities"
  Abstract: We present a method called TIMEMACHINE to generate a timeline of events and relations for entities in a knowledge base. For example for an actor, such a timeline should show the most important professional and personal milestones and relationships such as works, awards, collaborations, and family relationships. We develop three orthogonal timeline quality criteria that an ideal timeline should satisfy: (1) it shows events that are relevant to the entity; (2) it shows events that are temporally diverse, so they distribute along the time axis, avoiding visual crowding and allowing for easy user interaction, such as zooming in and out; and (3) it shows events that are content diverse, so they contain many different types of events (e.g., for an actor, it should show movies and marriages and awards, not just movies). We present an algorithm to generate such timelines for a given time period and screen size, based on submodular optimization and web-co-occurrence statistics with provable performance guarantees. A series of user studies using Mechanical Turk shows that all three quality criteria are crucial to produce quality timelines and that our algorithm significantly outperforms various baseline and state-of-the-art methods.

Connected through hyperedge HE-1 (induced by source paper "joint event extraction along shortest dependency paths using graph convolutional networks"):
  with members:
    - "jointly multiple events extraction via attention based graph information aggregation"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "rapid customization for event extraction"
    - "encoding sentences with graph convolutional networks for semantic role labeling"
    - "semi supervised classification with graph convolutional networks"
    - "joint extraction of events and entities within a document context"
    - "a compare aggregate model for matching text sequences"

Connected through hyperedge HE-2 (induced by source paper "eventkg the hub of event knowledge on the web and biographical timeline generation"):
  with members:
    - "towards better understanding researcher strategies in cross lingual event analytics"
    - "eventkg tl creating cross lingual timelines from an event centric knowledge graph"
    - "eventkg a multilingual event centric temporal knowledge graph"
    - "multiwiki interlingual text passage alignment in wikipedia"
    - "ongoing events in wikipedia a cross lingual case study"
    - "learning to generate one sentence biographies from wikidata"
    - "neural text generation from structured data with application to the biography domain"
    - "history by diversity helping historians search news archives"

Connected through hyperedge HE-3 (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"
    - "statistically significant detection of linguistic change"
    - "that s sick dude automatic identification of word sense change across different timescales"
    - "timeline generation tracking individuals on twitter"

Connected through hyperedge HE-4 (induced by source paper "entity summarization state of the art and future challenges"):
  with members:
    - "eventkg the hub of event knowledge on the web and biographical timeline generation"
    - "neural wikipedian generating textual summaries from knowledge base triples"
    - "learning to generate one sentence biographies from wikidata"
    - "neural text generation from structured data with application to the biography domain"
    - "graph summarization methods and applications a survey"
    - "leveraging usage data for linked data movie entity summarization"
    - "lexrank graph based lexical centrality as salience in text summarization"

Connected through hyperedge HE-5 (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"
    - "timeline generation tracking individuals on twitter"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-6 (induced by source paper "eventkg a multilingual event centric temporal knowledge graph"):
  with members:
    - "inferring missing categorical information in noisy and sparse web markup"
    - "eventkg tl creating cross lingual timelines from an event centric knowledge graph"
    - "multiwiki interlingual text passage alignment in wikipedia"
    - "ongoing events in wikipedia a cross lingual case study"
    - "icrawl improving the freshness of web collections by integrating social web and focused web crawling"

Connected through hyperedge HE-7 (induced by source paper "provenance for interactive visualizations"):
  with members:
    - "seq2seq vis a visual debugging tool for sequence to sequence models"
    - "smoke fine grained lineage at interactive speed"
    - "algorithms for provisioning queries and analytics"
    - "rapid sampling for visualizations with ordering guarantees"

Connected through hyperedge HE-8 (induced by source paper "eventkg tl creating cross lingual timelines from an event centric knowledge graph"):
  with members:
    - "eventkg a multilingual event centric temporal knowledge graph"
    - "multiwiki interlingual text passage alignment in wikipedia"
    - "ongoing events in wikipedia a cross lingual case study"
    - "icrawl improving the freshness of web collections by integrating social web and focused web crawling"

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.DB(Databases)