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: "ad hoc table retrieval using semantic similarity"
  Abstract: We introduce and address the problem of ad hoc table retrieval: answering a keyword query with a ranked list of tables.  This task is not only interesting on its own account, but is also being used as a core component in many other table-based information access scenarios, such as table completion or table mining. The main novel contribution of this work is a method for performing semantic matching between queries and tables. Specifically, we (i) represent queries and tables in multiple semantic spaces (both discrete sparse and continuous dense vector representations) and (ii) introduce various similarity measures for matching those semantic representations. We consider all possible combinations of semantic representations and similarity measures and use these as features in a supervised learning model. Using a purpose-built test collection based on Wikipedia tables, we demonstrate significant and substantial improvements over a state-of-the-art baseline.

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"
    - "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 "novel entity discovery from web tables"):
  with members:
    - "auto completion for data cells in relational tables"
    - "matchzoo a learning practicing and developing system for neural text matching"
    - "end to end neural entity linking"
    - "improving entity linking by modeling latent relations between mentions"
    - "on the fly table generation"
    - "deep joint entity disambiguation with local neural attention"
    - "the birth of collective memories analyzing emerging entities in text streams"

Connected through hyperedge HE-3 (induced by source paper "leveraging schema labels to enhance dataset search"):
  with members:
    - "dataset search a survey"
    - "entitables smart assistance for entity focused tables"
    - "enriching word vectors with subword information"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-4 (induced by source paper "dataset search a survey"):
  with members:
    - "interactive summarization and exploration of top aggregate query answers"
    - "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"
    - "entitables smart assistance for entity focused tables"
    - "navigating the data lake with datamaran automatically extracting structure from log datasets"

Connected through hyperedge HE-5 (induced by source paper "automatic table completion using knowledge base"):
  with members:
    - "table to text describing table region with natural language"
    - "on the fly table generation"
    - "entitables smart assistance for entity focused tables"
    - "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-6 (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"
    - "on the fly table generation"
    - "entitables smart assistance for entity focused tables"
    - "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-7 (induced by source paper "tablenet an approach for determining fine grained relations for wikipedia tables"):
  with members:
    - "node2vec scalable feature learning for networks"
    - "reasoning about entailment with neural attention"
    - "distributed representations of sentences and documents"
    - "neural machine translation by jointly learning to align and translate"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-8 (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"
    - "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)