human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "maps of computer science"):
  Abstract: We describe a practical approach for visual exploration of research papers. Specifically, we use the titles of papers from the DBLP database to create what we call maps of computer science (MoCS). Words and phrases from the paper titles are the cities in the map, and countries are created based on word and phrase similarity, calculated using co-occurrence. With the help of heatmaps, we can visualize the profile of a particular conference or journal over the base map. Similarly, heatmap profiles can be made of individual researchers or groups such as a department. The visualization system also makes it possible to change the data used to generate the base map. For example, a specific journal or conference can be used to generate the base map and then the heatmap overlays can be used to show the evolution of research topics in the field over the years. As before, individual researchers or research groups profiles can be visualized using heatmap overlays but this time over the journal or conference base map. Finally, research papers or abstracts easily generate visual abstracts giving a visual representation of the distribution of topics in the paper. We outline a modular and extensible system for term extraction using natural language processing techniques, and show the applicability of methods of information retrieval to calculation of term similarity and creation of a topic map. The system is available at mocs.cs.arizona.edu.

Includes members:
  Member "syntactic topic models":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "automatic evaluation of local topic quality")
  Member "putting recommendations on the map visualizing clusters and relations":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "research topics map rtopmap")
  Member "lexrank graph based lexical centrality as salience in text summarization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "generating representative headlines for news stories")
  Member "mapping topics and topic bursts in pnas":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "early detection of research trends")

Question: Please tell me which class the source paper 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)