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: "semantic search by latent ontological features"
  Abstract: Both named entities and keywords are important in defining the content of a text in which they occur. In particular, people often use named entities in information search. However, named entities have ontological features, namely, their aliases, classes, and identifiers, which are hidden from their textual appearance. We propose ontology-based extensions of the traditional Vector Space Model that explore different combinations of those latent ontological features with keywords for text retrieval. Our experiments on benchmark datasets show better search quality of the proposed models as compared to the purely keyword-based model, and their advantages for both text retrieval and representation of documents and queries.

Connected through hyperedge HE-1 (induced by source paper "data warehouse and decision support on integrated crop big data"):
  with members:
    - "a similarity measure for weaving patterns in textiles"

Connected through hyperedge HE-2 (induced by source paper "semantic search using spreading activation based on ontology"):
  with members:
    - "a generalized vector space model for ontology based information retrieval"
    - "structural consistency enabling xml keyword search to eliminate spurious results consistently"
    - "mining meaning from wikipedia"

Connected through hyperedge HE-3 (induced by source paper "machine learning based english sentiment analysis"):
  with members:
    - "detecting vietnamese opinion spam"
    - "discovering latent concepts and exploiting ontological features for semantic text search"

Connected through hyperedge HE-4 (induced by source paper "using entity relations for opinion mining of vietnamese comments"):
  with members:
    - "detecting vietnamese opinion spam"
    - "discovering latent concepts and exploiting ontological features for semantic text search"

Connected through hyperedge HE-5 (induced by source paper "discovering latent information by spreading activation algorithm for document retrieval"):
  with members:
    - "collective evolutionary concept distance based query expansion for effective web document retrieval"

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)