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 "escape efficiently counting all 5 vertex subgraphs"):
  Abstract: Counting the frequency of small subgraphs is a fundamental technique in network analysis across various domains, most notably in bioinformatics and social networks. The special case of triangle counting has received much attention. Getting results for 4-vertex or 5-vertex patterns is highly challenging, and there are few practical results known that can scale to massive sizes. #R##N#We introduce an algorithmic framework that can be adopted to count any small pattern in a graph and apply this framework to compute exact counts for \emph{all} 5-vertex subgraphs. Our framework is built on cutting a pattern into smaller ones, and using counts of smaller patterns to get larger counts. Furthermore, we exploit degree orientations of the graph to reduce runtimes even further. These methods avoid the combinatorial explosion that typical subgraph counting algorithms face. We prove that it suffices to enumerate only four specific subgraphs (three of them have less than 5 vertices) to exactly count all 5-vertex patterns. #R##N#We perform extensive empirical experiments on a variety of real-world graphs. We are able to compute counts of graphs with tens of millions of edges in minutes on a commodity machine. To the best of our knowledge, this is the first practical algorithm for $5$-vertex pattern counting that runs at this scale. A stepping stone to our main algorithm is a fast method for counting all $4$-vertex patterns. This algorithm is typically ten times faster than the state of the art $4$-vertex counters.

Includes members:
  Member "higher order organization of complex networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "hypergraph motifs concepts algorithms and discoveries")
  Member "scalable motif aware graph clustering":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "hypergraph motifs concepts algorithms and discoveries")
  Member "fast parallel graphlet counting for large networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "the resistance perturbation distance a metric for the analysis of dynamic networks")
  Member "beyond triangles a distributed framework for estimating 3 profiles of large graphs":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a fast counting method for 6 motifs with low connectivity")
  Member "distributed estimation of graph 4 profiles":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a fast counting method for 6 motifs with low connectivity")
  Member "counting triangles in massive graphs with mapreduce":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "parallel algorithms for small subgraph counting")
  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 "finding the hierarchy of dense subgraphs using nucleus decompositions":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "the power of pivoting for exact clique counting")

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.SI(Social and Information Networks)