human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "knowledge representation analysis of graph mining"):
  Abstract: Many problems, especially those with a composite structure, can naturally be expressed in higher order logic. From a KR perspective modeling these problems in an intuitive way is a challenging task. In this paper we study the graph mining problem as an example of a higher order problem. In short, this problem asks us to find a graph that frequently occurs as a subgraph among a set of example graphs. We start from the problem's mathematical definition to solve it in three state-of-the-art specification systems. For IDP and ASP, which have no native support for higher order logic, we propose the use of encoding techniques such as the disjoint union technique and the saturation technique. ProB benefits from the higher order support for sets. We compare the performance of the three approaches to get an idea of the overhead of the higher order support. #R##N#We propose higher-order language extensions for IDP-like specification languages and discuss what kind of solver support is needed. Native higher order shifts the burden of rewriting specifications using encoding techniques from the user to the solver itself.

Includes members:
  Member "semantics of templates in a compositional framework for building logics":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "knowledge of uncertain worlds programming with logical constraints")
  Member "predicate logic as a modelling language the idp system":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multi shot asp solving with clingo")
  Member "mining patterns in networks using homomorphism":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "mining frequent neighborhood patterns in a large labeled graph")

Question: Please tell me which class the source paper 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.LO(Logic in Computer Science)