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 "kaleido an efficient out of core graph mining system on a single machine"):
  Abstract: Graph mining is one of the most important categories of graph algorithms. #R##N#However, exploring the subgraphs of an input graph produces a huge amount of intermediate data. #R##N#The 'think like a vertex' programming paradigm, pioneered by Pregel, cannot readily formulate mining problems, which is designed to produce graph computation problems like PageRank. #R##N#Existing mining systems like Arabesque and RStream need large amounts of computing and memory resources. #R##N#In this paper, we present Kaleido, an efficient single machine, out-of-core graph mining system which treats disks as an extension of memory. #R##N#Kaleido treats intermediate data in graph mining tasks as a tensor and adopts a succinct data structure for the intermediate data. #R##N#Kaleido utilizes the eigenvalue of the adjacency matrix of a subgraph to efficiently solve the subgraph isomorphism problems with an acceptable constraint that the vertex number of a subgraph is less than 9. #R##N#Kaleido implements half-memory-half-disk storage for storing large intermediate data, which treats the disk as an extension of the memory. #R##N#Comparing with two state-of-the-art mining systems, Arabesque and RStream, Kaleido outperforms them by a GeoMean 12.3$\times$ and 40.0$\times$ respectively.

Includes members:
  Member "nscale neighborhood centric large scale graph analytics in the cloud":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "graph3s a simple speedy and scalable distributed graph processing system")
  Member "search space contraction in canonical labeling of graphs":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "graph iso auto morphism a divide conquer approach")

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.DC(Distributed, Parallel, and Cluster Computing)