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: "fixed parameter complexity of semantics for logic programs"
  Abstract: A decision problem is called parameterized if its input is a pair of strings. One of these strings is referred to as a parameter. The problem: given a propositional logic program P and a non-negative integer k, decide whether P has a stable model of size no more than k, is an example of a parameterized decision problem with k serving as a parameter. Parameterized problems that are NP-complete often become solvable in polynomial time if the parameter is fixed. The problem to decide whether a program P has a stable model of size no more than k, where k is fixed and not a part of input, can be solved in time O(mn^k), where m is the size of P and n is the number of atoms in P. Thus, this problem is in the class P. However, algorithms with the running time given by a polynomial of order k are not satisfactory even for relatively small values of k. #R##N#The key question then is whether significantly better algorithms (with the degree of the polynomial not dependent on k) exist. To tackle it, we use the framework of fixed-parameter complexity. We establish the fixed-parameter complexity for several parameterized decision problems involving models, supported models and stable models of logic programs. We also establish the fixed-parameter complexity for variants of these problems resulting from restricting attention to Horn programs and to purely negative programs. Most of the problems considered in the paper have high fixed-parameter complexity. Thus, it is unlikely that fixing bounds on models (supported models, stable models) will lead to fast algorithms to decide the existence of such models.

Connected through hyperedge HE-1 (induced by source paper "structural decompositions of epistemic logic programs"):
  with members:
    - "epistemic logic programs a different world view"
    - "treewidth and counting projected answer sets"
    - "te eth lower bounds for qbfs of bounded treewidth"
    - "splitting epistemic logic programs"
    - "answer set solving with bounded treewidth revisited"
    - "the power of non ground rules in answer set programming"
    - "combining treewidth and backdoors for csp"
    - "clique width and directed width measures for answer set programming"

Connected through hyperedge HE-2 (induced by source paper "clique width and directed width measures for answer set programming"):
  with members:
    - "directed width measures and monotonicity of directed graph searching"
    - "a trichotomy for regular simple path queries on graphs"
    - "digraph complexity measures and applications in formal language theory"
    - "backdoors to tractable answer set programming"
    - "trichotomy and dichotomy results on the complexity of reasoning with disjunctive logic programs"
    - "a hierarchy of tractable subsets for computing stable models"

Connected through hyperedge HE-3 (induced by source paper "computing stable models worst case performance estimates"):
  with members:
    - "on the problem of computing the well founded semantics"
    - "computing large and small stable models"
    - "dlv a system for declarative problem solving"
    - "extremal problems in logic programming and stable model computation"

Connected through hyperedge HE-4 (induced by source paper "computing large and small stable models"):
  with members:
    - "stable models and an alternative logic programming paradigm"

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)