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: "extracting unsatisfiable cores for ltl via temporal resolution"
  Abstract: Unsatisfiable cores (UCs) are a well established means for debugging in a declarative setting. Still, there are few tools that perform automated extraction of UCs for LTL. Existing tools compute a UC as an unsatisfiable subset of the set of top-level conjuncts of an LTL formula. Using resolution graphs to extract UCs is common in other domains such as SAT. In this article we construct and optimize resolution graphs for temporal resolution as implemented in the temporal resolution-based solver TRP++, and we use them to extract UCs for propositional LTL. The resulting UCs are more fine-grained than the UCs obtained from existing tools because UC extraction also simplifies top-level conjuncts instead of treating them as atomic entities. For example, given an unsatisfiable LTL formula of the form $$ \phi \equiv {(\mathbf{G}{ \psi })}\wedge {\mathbf{F}{ \psi ' }}$$??(G?)?F?? existing tools return $$ \phi $$? as a UC irrespective of the complexity of $$ \psi $$? and $$ \psi ' $$??, whereas the approach presented in this article continues to remove parts not required for unsatisfiability inside $$ \psi $$? and $$ \psi ' $$??. Our approach also identifies groups of occurrences of a proposition that do not interact in a proof of unsatisfiability. We implement our approach in TRP++. Our experimental evaluation demonstrates that our approach (i) extracts UCs that are often significantly smaller than the input formula with an acceptable overhead and (ii) produces more fine-grained UCs than competing tools while remaining at least competitive in terms of run time and memory usage. The source code of our tool is publicly available.

Connected through hyperedge HE-1 (induced by source paper "enhancing unsatisfiable cores for ltl with information on temporal relevance"):
  with members:
    - "a fully automatic problem solver with human style output"
    - "on the complexity of computing minimal unsatisfiable ltl formulas"
    - "computing small unsatisfiable cores in satisfiability modulo theories"
    - "handling conflicts in depth first search for ltl tableau to debug compliance based languages"
    - "clausal temporal resolution"

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.LO(Logic in Computer Science)