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 "slim graph practical lossy graph compression for approximate graph processing storage and analytics"):
  Abstract: We propose Slim Graph: the first programming model and framework for practical lossy graph compression that facilitates high-performance approximate graph processing, storage, and analytics. Slim Graph enables the developer to express numerous compression schemes using small and programmable compression kernels that can access and modify local parts of input graphs. Such kernels are executed in parallel by the underlying engine, isolating developers from complexities of parallel programming. Our kernels implement novel graph compression schemes that preserve numerous graph properties, for example connected components, minimum spanning trees, or graph spectra. Finally, Slim Graph uses statistical divergences and other metrics to analyze the accuracy of lossy graph compression. We illustrate both theoretically and empirically that Slim Graph accelerates numerous graph algorithms, reduces storage used by graph datasets, and ensures high accuracy of results. Slim Graph may become the common ground for developing, executing, and analyzing emerging lossy graph compression schemes.

Includes members:
  Member "demystifying graph databases analysis and taxonomy of data organization system designs and graph queries":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "practice of streaming and dynamic graphs concepts models systems and parallelism")
  Member "graph processing on fpgas taxonomy survey challenges":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "practice of streaming and dynamic graphs concepts models systems and parallelism")
  Member "fatpaths routing in supercomputers data centers and clouds with low diameter networks when shortest paths fall short":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "practice of streaming and dynamic graphs concepts models systems and parallelism")
  Member "a modular benchmarking infrastructure for high performance and reproducible deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "practice of streaming and dynamic graphs concepts models systems and parallelism")
  Member "active access a mechanism for high performance distributed data centric computations":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "demystifying graph databases analysis and taxonomy of data organization system designs and graph queries")
  Member "communication efficient jaccard similarity for high performance distributed genome comparisons":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "the parallelism motifs of genomic data analysis")
  Member "local computation algorithms for spanners":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "local algorithms for sparse spanning graphs")
  Member "survey and taxonomy of lossless graph compression and space efficient graph representations":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "structural indexing for conjunctive path queries")

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)