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: "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.

Connected through hyperedge HE-1 (induced by source paper "web table extraction retrieval and augmentation a survey"):
  with members:
    - "auto completion for data cells in relational tables"
    - "recommending related tables"
    - "tablenet an approach for determining fine grained relations for wikipedia tables"
    - "generating titles for web tables"
    - "ad hoc table retrieval using semantic similarity"
    - "on the fly table generation"
    - "entitables smart assistance for entity focused tables"
    - "design patterns for fusion based object retrieval"

Connected through hyperedge HE-2 (induced by source paper "a hierarchical model for data to text generation"):
  with members:
    - "data to text generation with entity modeling"
    - "copy mechanism and tailored training for character based data to text generation"
    - "tabvec table vectors for classification of web tables"
    - "learning neural templates for text generation"
    - "neural arithmetic logic units"
    - "a call for clarity in reporting bleu scores"
    - "data to text generation with content selection and planning"
    - "survey of the state of the art in natural language generation core tasks applications and evaluation"

Connected through hyperedge HE-3 (induced by source paper "auto completion for data cells in relational tables"):
  with members:
    - "ad hoc table retrieval using semantic similarity"
    - "on the fly table generation"
    - "entitables smart assistance for entity focused tables"
    - "distributed representations of words and phrases and their compositionality"

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)