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: "strategies for stable merge sorting"
  Abstract: We introduce new stable, natural merge sort algorithms, called 2-merge sort and $\alpha$-merge sort. We prove upper and lower bounds for several merge sort algorithms, including Timsort, Shiver's sort, $\alpha$-stack sorts, and our new 2-merge and $\alpha$-merge sorts. The upper and lower bounds have the forms $c \cdot n \log m$ and $c \cdot n \log n$ for inputs of length $n$ comprising $m$ runs. For Timsort, we prove a lower bound of $ (1.5 - o(1)) n \log n $. For 2-merge sort, we prove optimal upper and lower bounds of approximately $ (1.089 \pm o(1))n \log m $. We prove similar asymptotically matching upper and lower bounds for $\alpha$-merge sort, when $\varphi < \alpha < 2$, where $\varphi$ is the golden ratio. These merge strategies can be used for any stable merge sort, not just natural merge sorts. #R##N#The new 2-merge and $\alpha$-merge sorts have better worst-case merge cost upper bounds and are slightly simpler to implement than the widely-used Timsort; they also perform better in experiments.

Connected through hyperedge HE-1 (induced by source paper "nn sort neural network based data distribution aware sorting"):
  with members:
    - "a model for learned bloom filters and optimizing by sandwiching"
    - "worst case efficient sorting with quickmergesort"
    - "the case for learned index structures"
    - "sorting with gpus a survey"
    - "tensorflow a system for large scale machine learning"
    - "adadelta an adaptive learning rate method"
    - "gpu sample sort"

Connected through hyperedge HE-2 (induced by source paper "general criteria for successor rules to efficiently generate binary de bruijn sequences"):
  with members:
    - "on binary de bruijn sequences from lfsrs with arbitrary characteristic polynomials"

Connected through hyperedge HE-3 (induced by source paper "adaptive shivers sort an alternative sorting algorithm"):
  with members:
    - "on the worst case complexity of timsort"
    - "nearly optimal mergesorts fast practical sorting methods that optimally adapt to existing runs"
    - "on compressing permutations and adaptive sorting"

Connected through hyperedge HE-4 (induced by source paper "on the worst case complexity of timsort"):
  with members:
    - "nearly optimal mergesorts fast practical sorting methods that optimally adapt to existing runs"
    - "on compressing permutations and adaptive sorting"

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)