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: "gpu sample sort"
  Abstract: In this paper, we present the design of a sample sort algorithm for manycore GPUs. Despite being one of the most efficient comparison-based sorting algorithms for distributed memory architectures its performance on GPUs was previously unknown. For uniformly distributed keys our sample sort is at least 25% and on average 68% faster than the best comparison-based sorting algorithm, GPU Thrust merge sort, and on average more than 2 times faster than GPU quicksort. Moreover, for 64-bit integer keys it is at least 63% and on average 2 times faster than the highly optimized GPU Thrust radix sort that directly manipulates the binary representation of keys. Our implementation is robust to different distributions and entropy levels of keys and scales almost linearly with the input size. These results indicate that multi-way techniques in general and sample sort in particular achieve substantially better performance than two-way merge sort and quicksort.

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"
    - "strategies for stable merge sorting"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "engineering faster sorters for small sets of elements"):
  with members:
    - "scalable string and suffix sorting algorithms techniques and tools"
    - "in place parallel super scalar samplesort ips 4 o"
    - "robust massively parallel sorting"
    - "twenty five comparators is optimal when sorting nine inputs and twenty nine for ten"
    - "parallel string sample sort"

Connected through hyperedge HE-3 (induced by source paper "a memory bandwidth efficient hybrid radix sort on gpus"):
  with members:
    - "massively parallel sort merge joins in main memory multi core database systems"
    - "deterministic sample sort for gpus"

Connected through hyperedge HE-4 (induced by source paper "an efficient multiway mergesort for gpu architectures"):
  with members:
    - "a performance comparison of sort and scan libraries for gpus"
    - "merge path a visually intuitive approach to parallel merging"

Connected through hyperedge HE-5 (induced by source paper "sorting with gpus a survey"):
  with members:
    - "deterministic sample sort for gpus"

Connected through hyperedge HE-6 (induced by source paper "how good is multi pivot quicksort"):
  with members:
    - "a note on multipivot quicksort"
    - "blockquicksort how branch mispredictions don t affect quicksort"
    - "analysis of pivot sampling in dual pivot quicksort a holistic analysis of yaroslavskiy s partitioning scheme"
    - "analysis of branch misses in quicksort"
    - "average case and distributional analysis of dual pivot quicksort"
    - "optimal partitioning for dual pivot quicksort"
    - "average case analysis of java 7 s dual pivot quicksort"

Connected through hyperedge HE-7 (induced by source paper "a new data layout for set intersection on gpus"):
  with members:
    - "fast evaluation of union intersection expressions"

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)