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 "a characterisation of s box fitness landscapes in cryptography"):
  Abstract: Substitution Boxes (S-boxes) are nonlinear objects often used in the design of cryptographic algorithms. The design of high quality S-boxes is an interesting problem that attracts a lot of attention. Many attempts have been made in recent years to use heuristics to design S-boxes, but the results were often far from the previously known best obtained ones. Unfortunately, most of the effort went into exploring different algorithms and fitness functions while little attention has been given to the understanding why this problem is so difficult for heuristics. In this paper, we conduct a fitness landscape analysis to better understand why this problem can be difficult. Among other, we find that almost each initial starting point has its own local optimum, even though the networks are highly interconnected.

Includes members:
  Member "on the performance of multi objective estimation of distribution algorithms for combinatorial problems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on resampling vs adjusting probabilistic graphical models in estimation of distribution algorithms")
  Member "local optima networks a new model of combinatorial fitness landscapes":
    also connected through other hyperedges:
      (none)
  Member "first improvement vs best improvement local optima networks of nk landscapes":
    also connected through other hyperedges:
      - Hyperedge HE-2 (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-3 (induced by source paper "local optima networks a new model of combinatorial fitness landscapes")
  Member "local optima networks landscape autocorrelation and heuristic search performance":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "local optima networks a new model of combinatorial fitness landscapes")
  Member "local optima networks of nk landscapes with neutrality":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "local optima networks a new model of combinatorial fitness landscapes")
  Member "local optima networks of the quadratic assignment problem":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "is perturbation an effective restart strategy")
  Member "the connectivity of nk landscapes basins a network analysis":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "local optima networks a new model of combinatorial fitness landscapes")

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)