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: "entity ranking in wikipedia"
  Abstract: The traditional entity extraction problem lies in the ability of extracting named entities from plain text using natural language processing techniques and intensive training from large document collections. Examples of named entities include organisations, people, locations, or dates. There are many research activities involving named entities; we are interested in entity ranking in the field of information retrieval. In this paper, we describe our approach to identifying and ranking entities from the INEX Wikipedia document collection. Wikipedia offers a number of interesting features for entity identification and ranking that we first introduce. We then describe the principles and the architecture of our entity ranking system, and introduce our methodology for evaluation. Our preliminary results show that the use of categories and the link structure of Wikipedia, together with entity examples, can significantly improve retrieval effectiveness.

Connected through hyperedge HE-1 (induced by source paper "global entity ranking across multiple languages"):
  with members:
    - "high throughput and language agnostic entity disambiguation and linking on user generated data"
    - "learning from the news predicting entity popularity on twitter"
    - "klout score measuring influence across multiple social networks"

Connected through hyperedge HE-2 (induced by source paper "recognizing descriptive wikipedia categories for historical figures"):
  with members:
    - "deepwalk online learning of social representations"
    - "ranking of wikipedia articles in search engines revisited fair ranking for reasonable quality"

Connected through hyperedge HE-3 (induced by source paper "wikiometrics a wikipedia based ranking system"):
  with members:
    - "wikipedia ranking of world universities"
    - "towards physical laws for software architecture"
    - "two dimensional ranking of wikipedia articles"

Connected through hyperedge HE-4 (induced by source paper "galaxysearch discovering the knowledge of many by using wikipedia as a meta searchindex"):
  with members:
    - "analyzing and visualizing the semantic coverage of wikipedia and its authors"

Connected through hyperedge HE-5 (induced by source paper "mining meaning from wikipedia"):
  with members:
    - "use of wikipedia categories in entity ranking"
    - "synonym search in wikipedia synarcher"
    - "the google similarity distance"
    - "semantic similarity in a taxonomy an information based measure and its application to problems of ambiguity in natural language"

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.IR(Information Retrieval)