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 "table2vec neural word and entity embeddings for table population and retrieval"):
  Abstract: Tables contain valuable knowledge in a structured form. We employ neural language modeling approaches to embed tabular data into vector spaces. Specifically, we consider different table elements, such caption, column headings, and cells, for training word and entity embeddings. These embeddings are then utilized in three particular table-related tasks, row population, column population, and table retrieval, by incorporating them into existing retrieval models as additional semantic similarity signals. Evaluation results show that table embeddings can significantly improve upon the performance of state-of-the-art baselines.

Includes members:
  Member "tabvec table vectors for classification of web tables":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a hierarchical model for data to text generation")
  Member "ad hoc table retrieval using semantic similarity":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "web table extraction retrieval and augmentation a survey")
  Member "on the fly table generation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "web table extraction retrieval and augmentation a survey")
  Member "entitables smart assistance for entity focused tables":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "web table extraction retrieval and augmentation a survey")
  Member "design patterns for fusion based object retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "web table extraction retrieval and augmentation a survey")
  Member "distributed representations of words and phrases and their compositionality":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "answering table queries on the web using column keywords":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "web table extraction retrieval and augmentation a survey")

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)