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: "eventkg the hub of event knowledge on the web and biographical timeline generation"
  Abstract: One of the key requirements to facilitate the semantic analytics of information regarding contemporary and historical events on the Web, in the news and in social media is the availability of reference knowledge repositories containing comprehensive representations of events, entities and temporal relations. Existing knowledge graphs, with popular examples including DBpedia, YAGO and Wikidata, focus mostly on entity-centric information and are insufficient in terms of their coverage and completeness with respect to events and temporal relations. In this article we address this limitation, formalise the concept of a temporal knowledge graph and present its instantiation - EventKG. EventKG is a multilingual event-centric temporal knowledge graph that incorporates over 690 thousand events and over 2.3 million temporal relations obtained from several large-scale knowledge graphs and semi-structured sources and makes them available through a canonical RDF representation. Whereas popular entities often possess hundreds of relations within a temporal knowledge graph such as EventKG, generating a concise overview of the most important temporal relations for a given entity is a challenging task. In this article we demonstrate an application of EventKG to biographical timeline generation, where we adopt a distant supervision method to identify relations most relevant for an entity biography. Our evaluation results provide insights on the characteristics of EventKG and demonstrate the effectiveness of the proposed biographical timeline generation method.

Connected through hyperedge HE-1 (induced by source paper "esbm an entity summarization benchmark"):
  with members:
    - "entity summarization state of the art and future challenges"
    - "leveraging usage data for linked data movie entity summarization"

Connected through hyperedge HE-2 (induced by source paper "entity summarization state of the art and future challenges"):
  with members:
    - "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"
    - "timemachine timeline generation for knowledge base entities"
    - "leveraging usage data for linked data movie entity summarization"
    - "lexrank graph based lexical centrality as salience in text summarization"

Connected through hyperedge HE-3 (induced by source paper "happening happen predict infer event series completion in a knowledge graph"):
  with members:
    - "inferring missing categorical information in noisy and sparse web markup"
    - "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"
    - "stranse a novel embedding model of entities and relationships in knowledge bases"
    - "proje embedding projection for knowledge graph completion"
    - "there is no deadline time evolution of wikipedia discussions"

Connected through hyperedge HE-4 (induced by source paper "data4urbanmobility towards holistic data analytics for mobility applications in urban regions"):
  with members:
    - "inferring missing categorical information in noisy and sparse web markup"
    - "eventkg a multilingual event centric temporal knowledge graph"
    - "depicting urban boundaries from a mobility network of spatial interactions a case study of great britain with geo located twitter data"
    - "extracting event centric document collections from large scale web archives"
    - "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.CL(Computation and Language)