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 "communication efficient jaccard similarity for high performance distributed genome comparisons"):
  Abstract: Jaccard Similarity index is an important measure of the overlap of two sets, widely used in machine learning, computational genomics, information retrieval, and many other areas. However, little efforts have been made to develop a scalable and high-performance scheme for computing the Jaccard Similarity for today's large data sets. To address this issue, we design and implement SimilarityAtScale, the first communicationefficient distributed algorithm for computing the Jaccard Similarity. The key idea is to express the problem algebraically, as a sequence of matrix operations, and implement these operations with communication-avoiding distributed routines to minimize the amount of transferred data and ensure both high scalability and low latency. We then apply our algorithm to the problem of obtaining distances between whole-genome sequencing samples, a key part of modern metagenomics analysis and an evergrowing need due to the increasing availability of high-throughput DNA sequencing data. The resulting scheme is the first to enable accurate Jaccard distance derivations for massive datasets, using large-scale distributed-memory systems. We package our routines in a tool, called GenomeAtScale, that combines the proposed algorithm with tools for processing input sequences. Our evaluation on real data illustrates that one can use GenomeAtScale to effectively employ tens of thousands of processors to reach new frontiers in large-scale genomic and metagenomic analysis. While GenomeAtScale can be used to foster DNA research, the more general underlying SimilarityAtScale algorithm may be used for high-performance distributed similarity computations in other data analytics application domains.

Includes members:
  Member "generalized intersection over union a metric and a loss for bounding box regression":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "siamese box adaptive network for visual tracking")
  Member "maximally consistent sampling and the jaccard index of probability distributions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "inferring spatial uncertainty in object detection")
  Member "a note on the triangle inequality for the jaccard distance":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "generalized intersection over union a metric and a loss for bounding box regression")
  Member "scaling betweenness centrality using communication efficient sparse matrix multiplication":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a unified iteration space transformation framework for sparse and dense tensor algebra")
  Member "mathematical foundations of the graphblas":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "75 000 000 000 streaming inserts second using hierarchical hypersparse graphblas matrices")
  Member "exploiting multiple levels of parallelism in sparse matrix matrix multiplication":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a systematic survey of general sparse matrix matrix multiplication")
  Member "sparse tensor algebra as a parallel programming model":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "pasta a parallel sparse tensor algorithm benchmark suite")
  Member "parallel sparse matrix matrix multiplication and indexing implementation and experiments":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "the parallelism motifs of genomic data analysis")

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.CE(Computational Engineering, Finance, and Science)