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: "don t be strict in local search"
  Abstract: Local Search is one of the fundamental approaches to combinatorial optimization and it is used throughout AI. Several local search algorithms are based on searching the k-exchange neighborhood. This is the set of solutions that can be obtained from the current solution by exchanging at most k elements. As a rule of thumb, the larger k is, the better are the chances of finding an improved solution. However, for inputs of size n, a na\"ive brute-force search of the k-exchange neighborhood requires n to the power of O(k) time, which is not practical even for very small values of k. #R##N#Fellows et al. (IJCAI 2009) studied whether this brute-force search is avoidable and gave positive and negative answers for several combinatorial problems. They used the notion of local search in a strict sense. That is, an improved solution needs to be found in the k-exchange neighborhood even if a global optimum can be found efficiently. #R##N#In this paper we consider a natural relaxation of local search, called permissive local search (Marx and Schlotter, IWPEC 2009) and investigate whether it enhances the domain of tractable inputs. We exemplify this approach on a fundamental combinatorial problem, Vertex Cover. More precisely, we show that for a class of inputs, finding an optimum is hard, strict local search is hard, but permissive local search is tractable. #R##N#We carry out this investigation in the framework of parameterized complexity.

Connected through hyperedge HE-1 (induced by source paper "randomized contractions meet lean decompositions"):
  with members:
    - "reducing cmso model checking to highly connected graphs"
    - "clustering with local restrictions"
    - "definability equals recognizability for graphs of bounded treewidth"
    - "linear time kernelization for feedback vertex set"
    - "parameterized complexity dichotomy for steiner multicut"
    - "half integrality lp branching and fpt algorithms"
    - "minimum bisection is fixed parameter tractable"
    - "designing fpt algorithms for cut problems using randomized contractions"

Connected through hyperedge HE-2 (induced by source paper "exact algorithms via monotone local search"):
  with members:
    - "exact algorithms for maximum independent set"
    - "faster algorithms to enumerate hypergraph transversals"
    - "a faster fpt algorithm and a smaller kernel for block graph vertex deletion"
    - "faster exact and parameterized algorithm for feedback vertex set in tournaments"
    - "on feedback vertex set new measure and new structures"
    - "an fpt algorithm and a polynomial kernel for linear rankwidth 1 vertex deletion"
    - "unit interval editing is fixed parameter tractable"
    - "linear recognition of almost interval graphs"

Connected through hyperedge HE-3 (induced by source paper "parameterized complexity results for exact bayesian network structure learning"):
  with members:
    - "on the hardness of losing weight"
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "a transformational characterization of equivalent bayesian network structures"
    - "learning bayesian network structure from massive datasets the sparse candidate algorithm"
    - "a complete anytime algorithm for treewidth"
    - "a simple approach for finding the globally optimal bayesian network structure"
    - "algorithms and complexity results for exact bayesian structure learning"
    - "ordering based search a simple and effective algorithm for learning bayesian networks"

Connected through hyperedge HE-4 (induced by source paper "improved approximation for 3 dimensional matching via bounded pathwidth local search"):
  with members:
    - "large neighborhood local search for the maximum set packing problem"
    - "quasi polynomial local search for restricted max min fair allocation"

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.DS(Data Structures and Algorithms)