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: "on the fly table generation"
  Abstract: Many information needs revolve around entities, which would be better answered by summarizing results in a tabular format, rather than presenting them as a ranked list. Unlike previous work, which is limited to retrieving existing tables, we aim to answer queries by automatically compiling a table in response to a query. We introduce and address the task of on-the-fly table generation: given a query, generate a relational table that contains relevant entities (as rows) along with their key properties (as columns). This problem is decomposed into three specific subtasks: (i) core column entity ranking, (ii) schema determination, and (iii) value lookup. We employ a feature-based approach for entity ranking and schema determination, combining deep semantic features with task-specific signals. We further show that these two subtasks are not independent of each other and can assist each other in an iterative manner. For value lookup, we combine information from existing tables and a knowledge base. Using two sets of entity-oriented queries, we evaluate our approach both on the component level and on the end-to-end table generation task.

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"
    - "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"
    - "ad hoc table retrieval using semantic similarity"
    - "improving entity linking by modeling latent relations between mentions"
    - "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 "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"
    - "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-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"
    - "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-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"
    - "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-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"
    - "entitables smart assistance for entity focused tables"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-7 (induced by source paper "smarttable a spreadsheet program with intelligent assistance"):
  with members:
    - "ad hoc table retrieval using semantic similarity"
    - "entitables smart assistance for entity focused tables"
    - "compositional semantic parsing on semi structured tables"

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)