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: "lin kernighan heuristic adaptations for the generalized traveling salesman problem"
  Abstract: The Lin-Kernighan heuristic is known to be one of the most successful heuristics for the Traveling Salesman Problem (TSP). It has also proven its efficiency in application to some other problems. In this paper, we discuss possible adaptations of TSP heuristics for the generalized traveling salesman problem (GTSP) and focus on the case of the Lin-Kernighan algorithm. At first, we provide an easy-to-understand description of the original Lin-Kernighan heuristic. Then we propose several adaptations, both trivial and complicated. Finally, we conduct a fair competition between all the variations of the Lin-Kernighan adaptation and some other GTSP heuristics. It appears that our adaptation of the Lin-Kernighan algorithm for the GTSP reproduces the success of the original heuristic. Different variations of our adaptation outperform all other heuristics in a wide range of trade-offs between solution quality and running time, making Lin-Kernighan the state-of-the-art GTSP local search.

Connected through hyperedge HE-1 (induced by source paper "conditional markov chain search for the generalised travelling salesman problem for warehouse order picking"):
  with members:
    - "conditional markov chain search for the simple plant location problem improves upper bounds on twelve korkel ghosh instances"
    - "algorithm configuration learning policies for the quick termination of poor performers"
    - "the generalized traveling salesman problem solved with ant algorithms"
    - "markov chain methods for the bipartite boolean quadratic programming problem"
    - "efficient local search algorithms for known and new neighborhoods for the generalized traveling salesman problem"
    - "a memetic algorithm for the generalized traveling salesman problem"
    - "empirical evaluation of construction heuristics for the multidimensional assignment problem"

Connected through hyperedge HE-2 (induced by source paper "a memetic algorithm based on breakout local search for the generalized travelling salesman problem"):
  with members:
    - "efficient local search algorithms for known and new neighborhoods for the generalized traveling salesman problem"
    - "a memetic algorithm for the generalized traveling salesman problem"

Connected through hyperedge HE-3 (induced by source paper "efficient local search algorithms for known and new neighborhoods for the generalized traveling salesman problem"):
  with members:
    - "the generalized traveling salesman problem solved with ant algorithms"
    - "local search heuristics for the multidimensional assignment problem"
    - "a memetic algorithm for the generalized traveling salesman problem"
    - "generalized traveling salesman problem reduction algorithms"

Connected through hyperedge HE-4 (induced by source paper "an efficient hybrid ant colony system for the generalized traveling salesman problem"):
  with members:
    - "the generalized traveling salesman problem solved with ant algorithms"
    - "efficient local search algorithms for known and new neighborhoods for the generalized traveling salesman problem"
    - "local search heuristics for the multidimensional assignment problem"
    - "a memetic algorithm for the generalized traveling salesman problem"

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)