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: "new bounds on optimal sorting networks"
  Abstract: We present new parallel sorting networks for $17$ to $20$ inputs. For $17, 19,$ and $20$ inputs these new networks are faster (i.e., they require less computation steps) than the previously known best networks. Therefore, we improve upon the known upper bounds for minimal depth sorting networks on $17, 19,$ and $20$ channels. Furthermore, we show that our sorting network for $17$ inputs is optimal in the sense that no sorting network using less layers exists. This solves the main open problem of [D. Bundala & J. Za\'vodn\'y. Optimal sorting networks, Proc. LATA 2014].

Connected through hyperedge HE-1 (induced by source paper "joint size and depth optimization of sorting networks"):
  with members:
    - "sorting networks to the end and back again"
    - "optimal sorting networks"

Connected through hyperedge HE-2 (induced by source paper "sat encodings for sorting networks single exception sorting networks and e halvers"):
  with members:
    - "sorting networks to the end and back again"
    - "zig zag sort a simple deterministic data oblivious sorting algorithm running in o n log n time"

Connected through hyperedge HE-3 (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"
    - "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"
    - "the quest for optimal sorting networks efficient generation of two layer prefixes"
    - "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-4 (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"
    - "the quest for optimal sorting networks efficient generation of two layer prefixes"
    - "twenty five comparators is optimal when sorting nine inputs and twenty nine for ten"
    - "optimal sorting networks"

Connected through hyperedge HE-5 (induced by source paper "even faster sorting of not only integers"):
  with members:
    - "blockquicksort how branch mispredictions don t affect quicksort"
    - "sorting data on ultra large scale with raduls new incarnation of radix sort"
    - "kmc 2 fast and resource frugal k mer counting"

Connected through hyperedge HE-6 (induced by source paper "sorting networks to the end and back again"):
  with members:
    - "optimal depth sorting networks"
    - "applying sorting networks to synthesize optimized sorting libraries"
    - "sorting networks the end game"
    - "the quest for optimal sorting networks efficient generation of two layer prefixes"
    - "optimal sorting networks"

Connected through hyperedge HE-7 (induced by source paper "applying sorting networks to synthesize optimized sorting libraries"):
  with members:
    - "sorting networks the end game"
    - "the quest for optimal sorting networks efficient generation of two layer prefixes"
    - "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 "itemset isomorphism gi hard"):
  with members:
    - "optimal depth sorting networks"
    - "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.DM(Discrete Mathematics)