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 "speeding up the construction of slow adaptive walks"):
  Abstract: An algorithm (bliss) is proposed to speed up the construction of slow adaptive walks. Slow adaptive walks are adaptive walks biased towards closer points or smaller move steps. They were previously introduced to explore a search space, e.g. to detect potential local optima or to assess the ruggedness of a fitness landscape. To avoid the quadratic cost of computing Hamming distance (HD) for all-pairs of strings in a set in order to find the set of closest strings for each string, strings are sorted and clustered by bliss such that similar strings are more likely to get paired off for HD computation. To efficiently arrange the strings by similarity, bliss employs the idea of shared non-overlapping position specific subsequences between strings which is inspired by an alignment-free protein sequence comparison algorithm. Tests are performed to evaluate the quality of b-walks, i.e. slow adaptive walks constructed from the output of bliss, on enumerated search spaces. Finally, b-walks are applied to explore larger search spaces with the help of Wang-Landau sampling.

Includes members:
  Member "search space analysis with wang landau sampling and slow adaptive walks":
    also connected through other hyperedges:
      (none)
  Member "what can we learn from slow self avoiding adaptive walks by an infinite radius search algorithm":
    also connected through other hyperedges:
      (none)

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)