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: "termite a system for tunneling through heterogeneous data"
  Abstract: Data-driven analysis is important in virtually every modern organization. Yet, most data is underutilized because it remains locked in silos inside of organizations; large organizations have thousands of databases, and billions of files that are not integrated together in a single, queryable repository. Despite 40+ years of continuous effort by the database community, data integration still remains an open challenge. In this paper, we advocate a different approach: rather than trying to infer a common schema, we aim to find another common representation for diverse, heterogeneous data. Specifically, we argue for an embedding (i.e., a vector space) in which all entities, rows, columns, and paragraphs are represented as points. In the embedding, the distance between points indicates their degree of relatedness. We present Termite, a prototype we have built to learn the best embedding from the data. Because the best representation is learned, this allows Termite to avoid much of the human effort associated with traditional data integration tasks. On top of Termite, we have implemented a Termite-Join operator, which allows people to identify related concepts, even when these are stored in databases with different schemas and in unstructured data such as text files, webpages, etc. Finally, we show preliminary evaluation results of our prototype via a user study, and describe a list of future directions we have identified.

Connected through hyperedge HE-1 (induced by source paper "neo a learned query optimizer"):
  with members:
    - "skinnerdb regret bounded query evaluation via reinforcement learning"
    - "pre training neural networks with human demonstrations for deep reinforcement learning"
    - "learned cardinalities estimating correlated joins with deep learning"
    - "learning state representations for query optimization with deep reinforcement learning"
    - "deep reinforcement learning for join order enumeration"
    - "learning scheduling algorithms for data processing clusters"
    - "learning to optimize join queries with deep reinforcement learning"
    - "deep contextualized word representations"

Connected through hyperedge HE-2 (induced by source paper "retro relation retrofitting for in database machine learning on textual data"):
  with members:
    - "idel in database entity linking with neural embeddings"
    - "graph embedding techniques applications and performance a survey"
    - "how to evaluate word embeddings on importance of data efficiency and simple supervised tasks"
    - "a comprehensive survey of graph embedding problems techniques and applications"
    - "enriching word vectors with subword information"
    - "counter fitting word vectors to linguistic constraints"
    - "an ensemble method to produce high quality word embeddings"
    - "sparse overcomplete word vector representations"

Connected through hyperedge HE-3 (induced by source paper "flexible operator embeddings via deep learning"):
  with members:
    - "learned cardinalities estimating correlated joins with deep learning"
    - "deep reinforcement learning for join order enumeration"
    - "database agnostic workload management"
    - "deep contextualized word representations"
    - "helix accelerating human in the loop machine learning"
    - "towards a hands free query optimizer through deep learning"
    - "the case for learned index structures"
    - "wisedb a learning based workload management advisor for cloud databases"

Connected through hyperedge HE-4 (induced by source paper "local embeddings for relational data integration"):
  with members:
    - "data curation with deep learning vision towards self driving data curation"
    - "node2vec scalable feature learning for networks"

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.DB(Databases)