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 "motivo fast motif counting via succinct color coding and adaptive sampling"):
  Abstract: The randomized technique of color coding is behind state-of-the-art algorithms for estimating graph motif counts. Those algorithms, however, are not yet capable of scaling well to very large graphs with billions of edges. In this paper we develop novel tools for the `motif counting via color coding' framework. As a result, our new algorithm, Motivo, is able to scale well to larger graphs while at the same time provide more accurate graphlet counts than ever before. This is achieved thanks to two types of improvements. First, we design new succinct data structures that support fast common color coding operations, and a biased coloring trick that trades accuracy versus running time and memory usage. These adaptations drastically reduce the time and memory requirements of color coding. Second, we develop an adaptive graphlet sampling strategy, based on a fractional set cover problem, that breaks the additive approximation barrier of standard sampling. This strategy gives multiplicative approximations for all graphlets at once, allowing us to count not only the most frequent graphlets but also extremely rare ones. #R##N#To give an idea of the improvements, in $40$ minutes Motivo counts $7$-nodes motifs on a graph with $65$M nodes and $1.8$B edges; this is $30$ and $500$ times larger than the state of the art, respectively in terms of nodes and edges. On the accuracy side, in one hour Motivo produces accurate counts of $\approx \! 10.000$ distinct $8$-node motifs on graphs where state-of-the-art algorithms fail even to find the second most frequent motif. Our method requires just a high-end desktop machine. These results show how color coding can bring motif mining to the realm of truly massive graphs using only ordinary hardware.

Includes members:
  Member "subgraph counting color coding beyond trees":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a graphblas approach for subgraph counting")
  Member "waddling random walk fast and accurate mining of motif statistics in large graphs":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "improved mixing time for k subgraph sampling")
  Member "escape efficiently counting all 5 vertex subgraphs":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "hypergraph motifs concepts algorithms and discoveries")
  Member "a general framework for estimating graphlet statistics via random walk":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "factorized graph representations for semi supervised learning from sparse data")
  Member "a fast and provable method for estimating clique counts using tur an s theorem":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "hypergraph motifs concepts algorithms and discoveries")
  Member "a fast sampling method of exploring graphlet degrees of large directed and undirected graphs":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "graphlets versus node2vec and struc2vec in the task of network alignment")
  Member "path sampling a fast and provable method for estimating 4 vertex subgraph counts":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "the power of pivoting for exact clique counting")
  Member "efficiently estimating motif statistics of large networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "improved mixing time for k subgraph sampling")

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.DB(Databases)