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: "the quest for optimal sorting networks efficient generation of two layer prefixes"
  Abstract: Previous work identifying depth-optimal n-channel sorting networks for 9 a#x2264; n a#x2264; 16 is based on exploiting symmetries of the first two layers. However, the naive generate-and-test approach typically applied does not scale. This paper revisits the problem of generating two-layer prefixes modulo symmetries. An improved notion of symmetry is provided and a novel technique based on regular languages and graph isomorphism is shown to generate the set of non-symmetric representations. An empirical evaluation demonstrates that the new method outperforms the generate-and-test approach by orders of magnitude and easily scales until n = 40.

Connected through hyperedge HE-1 (induced by source paper "enhanced optimization with composite objectives and novelty selection"):
  with members:
    - "discovering evolutionary stepping stones through behavior domination"
    - "sorting networks to the end and back again"
    - "robots that can adapt like animals"
    - "illuminating search spaces by mapping elites"
    - "evolution of swarm robotics systems with novelty search"

Connected through hyperedge HE-2 (induced by source paper "optimal depth sorting networks"):
  with members:
    - "sorting networks to the end and back again"
    - "solving and verifying the boolean pythagorean triples problem via cube and conquer"
    - "new bounds on optimal sorting networks"
    - "computing the ramsey number r 4 3 3 using abstraction and symmetry breaking"
    - "applying sorting networks to synthesize optimized sorting libraries"
    - "sorting networks the end game"
    - "twenty five comparators is optimal when sorting nine inputs and twenty nine for ten"
    - "computer aided proof of erdos discrepancy properties"

Connected through hyperedge HE-3 (induced by source paper "an improved subsumption testing algorithm for the optimal size sorting network problem"):
  with members:
    - "sorting networks to the end and back again"
    - "new bounds on optimal sorting networks"
    - "twenty five comparators is optimal when sorting nine inputs and twenty nine for ten"
    - "optimal sorting networks"

Connected through hyperedge HE-4 (induced by source paper "sorting networks to the end and back again"):
  with members:
    - "optimal depth sorting networks"
    - "new bounds on optimal sorting networks"
    - "applying sorting networks to synthesize optimized sorting libraries"
    - "sorting networks the end game"
    - "optimal sorting networks"

Connected through hyperedge HE-5 (induced by source paper "sorting networks the final countdown"):
  with members:
    - "optimal depth sorting networks"
    - "towards optimal sorting networks the third level"
    - "practical algorithms for finding extremal sets"
    - "sorting networks the end game"

Connected through hyperedge HE-6 (induced by source paper "applying sorting networks to synthesize optimized sorting libraries"):
  with members:
    - "new bounds on optimal sorting networks"
    - "sorting networks the end game"
    - "twenty five comparators is optimal when sorting nine inputs and twenty nine for ten"
    - "optimal sorting networks"

Connected through hyperedge HE-7 (induced by source paper "sorting networks the end game"):
  with members:
    - "faster sorting networks for 17 19 and 20 inputs"
    - "twenty five comparators is optimal when sorting nine inputs and twenty nine for ten"
    - "optimal sorting networks"

Connected through hyperedge HE-8 (induced by source paper "new bounds on optimal sorting networks"):
  with members:
    - "sorting networks the end game"
    - "optimal sorting networks"

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)