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 "design evaluation and analysis of combinatorial optimization heuristic algorithms"):
  Abstract: Combinatorial optimization is widely applied in a number of areas nowadays. Unfortunately, many combinatorial optimization problems are NP-hard which usually means that they are unsolvable in practice. However, it is often unnecessary to have an exact solution. In this case one may use heuristic approach to obtain a near-optimal solution in some reasonable time. #R##N#We focus on two combinatorial optimization problems, namely the Generalized Traveling Salesman Problem and the Multidimensional Assignment Problem. The first problem is an important generalization of the Traveling Salesman Problem; the second one is a generalization of the Assignment Problem for an arbitrary number of dimensions. Both problems are NP-hard and have hosts of applications. #R##N#In this work, we discuss different aspects of heuristics design and evaluation. A broad spectrum of related subjects, covered in this research, includes test bed generation and analysis, implementation and performance issues, local search neighborhoods and efficient exploration algorithms, metaheuristics design and population sizing in memetic algorithm. #R##N#The most important results are obtained in the areas of local search and memetic algorithms for the considered problems. In both cases we have significantly advanced the existing knowledge on the local search neighborhoods and algorithms by systematizing and improving the previous results. We have proposed a number of efficient heuristics which dominate the existing algorithms in a wide range of time/quality requirements. #R##N#Several new approaches, introduced in our memetic algorithms, make them the state-of-the-art metaheuristics for the corresponding problems. Population sizing is one of the most promising among these approaches; it is expected to be applicable to virtually any memetic algorithm.

Includes members:
  Member "the generalized traveling salesman problem solved with ant algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "conditional markov chain search for the generalised travelling salesman problem for warehouse order picking")
  Member "a new approach to population sizing for memetic algorithms a case study for the multidimensional assignment problem":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "variable population memetic search a case study on the critical node problem")
  Member "local search heuristics for the multidimensional assignment problem":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "efficient local search algorithms for known and new neighborhoods for the generalized traveling salesman problem")
  Member "a memetic algorithm for the generalized traveling salesman problem":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "conditional markov chain search for the generalised travelling salesman problem for warehouse order picking")
  Member "empirical evaluation of construction heuristics for the multidimensional assignment problem":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "conditional markov chain search for the generalised travelling salesman problem for warehouse order picking")
  Member "a memetic algorithm for the multidimensional assignment problem":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a new approach to population sizing for memetic algorithms a case study for the multidimensional assignment problem")
  Member "generalized traveling salesman problem reduction algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "efficient local search algorithms for known and new neighborhoods for the generalized traveling salesman problem")

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.DS(Data Structures and Algorithms)