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: "distributed xml design"
  Abstract: A distributed XML document is an XML document that spans several machines or Web repositories. We assume that a distribution design of the document tree is given, providing an XML tree some of whose leaves are "docking points", to which XML subtrees can be attached. These subtrees may be provided and controlled by peers at remote locations, or may correspond to the result of function calls, e.g., Web services. If a global type τ, e.g. a DTD, is specified for a distributed document T, it would be most desirable to be able to break this type into a collection of local types, called a local typing, such that the document satisfies τ if and only if each peer (or function) satisfies its local type. In this paper we lay out the fundamentals of a theory of local typing and provide formal definitions of three main variants of locality: local typing, maximal local typing, and perfect typing, the latter being the most desirable. We study the following relevant decision problems: (i) given a typing for a design, determine whether it is local, maximal local, or perfect; (ii) given a design, establish whether a (maximal) local, or perfect typing does exist. For some of these problems we provide tight complexity bounds (polynomial space), while for the others we show exponential upper bounds. A main contribution is a polynomial-space algorithm for computing a perfect typing in this context, if it exists.

Connected through hyperedge HE-1 (induced by source paper "split correctness in information extraction"):
  with members:
    - "constant delay algorithms for regular document spanners"
    - "document spanners for extracting incomplete information expressiveness and complexity"
    - "joining extractions of regular expressions"
    - "neural architectures for named entity recognition"
    - "incremental knowledge base construction using deepdive"
    - "named entity recognition with bidirectional lstm cnns"
    - "parallel correctness and transferability for conjunctive queries"
    - "a sentimental education sentiment analysis using subjectivity summarization based on minimum cuts"

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)