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: "qunits queried units in database search"
  Abstract: Keyword search against structured databases has become a popular topic of investigation, since many users find structured queries too hard to express, and enjoy the freedom of a ``Google-like'' query box into which search terms can be entered. Attempts to address this problem face a fundamental dilemma. Database querying is based on the logic of predicate evaluation, with a precisely defined answer set for a given query. On the other hand, in an information retrieval approach, ranked query results have long been accepted as far superior to results based on boolean query evaluation. As a consequence, when keyword queries are attempted against databases, relatively ad-hoc ranking mechanisms are invented (if ranking is used at all), and there is little leverage from the large body of IR literature regarding how to rank query results. #R##N#Our proposal is to create a clear separation between ranking and database querying. This divides the problem into two parts, and allows us to address these separately. The first task is to represent the database, conceptually, as a collection of independent ``queried units'', or ``qunits'', each of which represents the desired result for some query against the database. The second task is to evaluate keyword queries against a collection of qunits, which can be treated as independent documents for query purposes, thereby permitting the use of standard IR techniques. We provide insights that encourage the use of this query paradigm, and discuss preliminary investigations into the efficacy of a qunits-based framework based on a prototype implementation.

Connected through hyperedge HE-1 (induced by source paper "bridging the semantic gap with sql query logs in natural language interfaces to databases"):
  with members:
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "sqlnet generating structured queries from natural language without reinforcement learning"
    - "an end to end neural natural language interface for databases"
    - "spider a large scale human labeled dataset for complex and cross domain semantic parsing and text to sql task"
    - "dataset for a neural natural language interface for databases nnlidb"
    - "learning a neural semantic parser from user feedback"
    - "language to logical form with neural attention"
    - "learning executable semantic parsers for natural language understanding"

Connected through hyperedge HE-2 (induced by source paper "knowledge rich natural language queries over structured biological databases"):
  with members:
    - "natural language generation enhances human decision making with uncertain information"

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)