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: "possible and certain answers for queries over order incomplete data"
  Abstract: To combine and query ordered data from multiple sources, one needs to handle uncertainty about the possible orderings. Examples of such "order-incomplete" data include integrated event sequences such as log entries; lists of properties (e.g., hotels and restaurants) ranked by an unknown function reflecting relevance or customer ratings; and documents edited concurrently with an uncertain order on edits. This paper introduces a query language for order-incomplete data, based on the positive relational algebra with order-aware accumulation. We use partial orders to represent order-incomplete data, and study possible and certain answers for queries in this context. We show that these problems are respectively NP-complete and coNP-complete, but identify many tractable cases depending on the query operators or input partial orders.

Connected through hyperedge HE-1 (induced by source paper "computing possible and certain answers over order incomplete data"):
  with members:
    - "computational social choice meets databases"
    - "topological sorting under regular constraints"
    - "determining possible and necessary winners under common voting rules given partial orders"
    - "taking the final step to a full dichotomy of the possible winner problem in pure scoring rules"
    - "towards a dichotomy for the possible winner problem in elections based on scoring rules"
    - "world set decompositions expressiveness and efficient algorithms"
    - "optimal aggregation algorithms for middleware"

Connected through hyperedge HE-2 (induced by source paper "top k querying of unknown values under order constraints"):
  with members:
    - "uncertainty in crowd data sourcing under structural constraints"
    - "a fast and practical method to estimate volumes of convex polytopes"
    - "on the complexity of mining itemsets from the crowd using taxonomies"
    - "human assisted graph search it s okay to ask questions"
    - "a unified approach to ranking in probabilistic databases"
    - "semantics and evaluation of top k queries in probabilistic databases"

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)