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 "joint size and depth optimization of sorting networks"):
  Abstract: Sorting networks are oblivious sorting algorithms with many interesting theoretical properties and practical applications. One of the related classical challenges is the search of optimal networks respect to size (number of comparators) of depth (number of layers). However, up to our knowledge, the joint size-depth optimality of small sorting networks has not been addressed before. This paper presents size-depth optimality results for networks up to $12$ channels. Our results show that there are sorting networks for $n\leq9$ inputs that are optimal in both size and depth, but this is not the case for $10$ and $12$ channels. For $n=10$ inputs, we were able to proof that optimal-depth optimal sorting networks with $7$ layers require $31$ comparators while optimal-size networks with $29$ comparators need $8$ layers. For $n=11$ inputs we show that networks with $8$ or $9$ layers require at least $35$ comparators (the best known upper bound for the minimal size). And for networks with $n=12$ inputs and $8$ layers we need $40$ comparators, while for $9$ layers the best known size is $39$.

Includes members:
  Member "sorting networks to the end and back again":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "enhanced optimization with composite objectives and novelty selection")
  Member "new bounds on optimal sorting networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "sat encodings for sorting networks single exception sorting networks and e halvers")
  Member "optimal sorting networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "optimal metastability containing sorting via parallel prefix computation")

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.DS(Data Structures and Algorithms)