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: "entitables smart assistance for entity focused tables"
  Abstract: Tables are among the most powerful and practical tools for organizing and working with data. Our motivation is to equip spreadsheet programs with smart assistance capabilities. We concentrate on one particular family of tables, namely, tables with an entity focus. We introduce and focus on two specifc tasks: populating rows with additional instances (entities) and populating columns with new headings. We develop generative probabilistic models for both tasks. For estimating the components of these models, we consider a knowledge base as well as a large table corpus. Our experimental evaluation simulates the various stages of the user entering content into an actual table. A detailed analysis of the results shows that the models' components are complimentary and that our methods outperform existing approaches from the literature.

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"
    - "table2vec neural word and entity embeddings for table population and retrieval"
    - "generating titles for web tables"
    - "ad hoc table retrieval using semantic similarity"
    - "on the fly table generation"
    - "design patterns for fusion based object retrieval"

Connected through hyperedge HE-2 (induced by source paper "leveraging schema labels to enhance dataset search"):
  with members:
    - "dataset search a survey"
    - "ad hoc table retrieval using semantic similarity"
    - "enriching word vectors with subword information"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-3 (induced by source paper "dataset search a survey"):
  with members:
    - "interactive summarization and exploration of top aggregate query answers"
    - "ad hoc table retrieval using semantic similarity"
    - "cuttlefish a lightweight primitive for adaptive query processing"
    - "on the fly table generation"
    - "everything you always wanted to know about a dataset studies in data summarisation"
    - "a survey on data collection for machine learning a big data ai integration perspective"
    - "neural vector spaces for unsupervised information retrieval"
    - "navigating the data lake with datamaran automatically extracting structure from log datasets"

Connected through hyperedge HE-4 (induced by source paper "automatic table completion using knowledge base"):
  with members:
    - "table to text describing table region with natural language"
    - "ad hoc table retrieval using semantic similarity"
    - "on the fly table generation"
    - "prep path based relevance from a probabilistic perspective in heterogeneous information networks"
    - "xgboost a scalable tree boosting system"
    - "adam a method for stochastic optimization"
    - "finding patterns in a knowledge base using keywords to compose table answers"
    - "convolutional neural networks for sentence classification"

Connected through hyperedge HE-5 (induced by source paper "table2vec neural word and entity embeddings for table population and retrieval"):
  with members:
    - "tabvec table vectors for classification of web tables"
    - "ad hoc table retrieval using semantic similarity"
    - "on the fly table generation"
    - "design patterns for fusion based object retrieval"
    - "distributed representations of words and phrases and their compositionality"
    - "answering table queries on the web using column keywords"

Connected through hyperedge HE-6 (induced by source paper "auto completion for data cells in relational tables"):
  with members:
    - "table2vec neural word and entity embeddings for table population and retrieval"
    - "ad hoc table retrieval using semantic similarity"
    - "on the fly table generation"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-7 (induced by source paper "recommending related tables"):
  with members:
    - "ad hoc table retrieval using semantic similarity"
    - "neural programmer inducing latent programs with gradient descent"
    - "harnessing the deep web present and future"

Connected through hyperedge HE-8 (induced by source paper "ad hoc table retrieval using semantic similarity"):
  with members:
    - "design patterns for fusion based object retrieval"
    - "word entity duet representations for document ranking"
    - "scientific table search using keyword queries"
    - "modelling user preferences using word embeddings for context aware venue recommendation"
    - "a dual embedding space model for document ranking"
    - "neural enquirer learning to query tables with natural language"
    - "line large scale information network embedding"
    - "deepwalk online learning of social representations"

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)