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 "local optima networks a new model of combinatorial fitness landscapes"):
  Abstract: This chapter overviews a recently introduced network-based model of combinatorial landscapes: Local Optima Networks (LON). The model compresses the information given by the whole search space into a smaller mathematical object that is a graph having as vertices the local optima and as edges the possible weighted transitions between them. Two definitions of edges have been proposed: basin-transition and escape-edges, which capture relevant topological features of the underlying search spaces. This network model brings a new set of metrics to characterize the structure of combinatorial landscapes, those associated with the science of complex networks. These metrics are described, and results are presented of local optima network extraction and analysis for two selected combinatorial landscapes: NK landscapes and the quadratic assignment problem. Network features are found to correlate with and even predict the performance of heuristic search algorithms operating on these problems.

Includes members:
  Member "first improvement vs best improvement local optima networks of nk landscapes":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a study of fitness landscapes for neuroevolution")
  Member "local optima networks and the performance of iterated local search":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a characterisation of s box fitness landscapes in cryptography")
  Member "local optima networks landscape autocorrelation and heuristic search performance":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a characterisation of s box fitness landscapes in cryptography")
  Member "communities of minima in local optima networks of combinatorial spaces":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "local optima networks and the performance of iterated local search")
  Member "local optima networks of nk landscapes with neutrality":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a characterisation of s box fitness landscapes in cryptography")
  Member "local optima networks of the quadratic assignment problem":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a characterisation of s box fitness landscapes in cryptography")
  Member "the connectivity of nk landscapes basins a network analysis":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a characterisation of s box fitness landscapes in cryptography")
  Member "a study of nk landscapes basins and local optima networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a study of fitness landscapes for neuroevolution")

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.NE(Neural and Evolutionary Computing)