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 "big graph mining frameworks and techniques"):
  Abstract: Abstract   Big graph mining is an important research area and it has attracted considerable attention. It allows to process, analyze, and extract meaningful information from large amounts of graph data. Big graph mining has been highly motivated not only by the tremendously increasing size of graphs but also by its huge number of applications. Such applications include bioinformatics, chemoinformatics and social networks. One of the most challenging tasks in big graph mining is pattern mining in big graphs. This task consists on using data mining algorithms to discover interesting, unexpected and useful patterns in large amounts of graph data. It aims also to provide deeper understanding of graph data. In this context, several graph processing frameworks and scaling data mining/pattern mining techniques have been proposed to deal with very big graphs. This paper gives an overview of existing data mining and graph processing frameworks that deal with very big graphs. Then it presents a survey of current researches in the field of data mining/pattern mining in big graphs and discusses the main research issues related to this field. It also gives a categorization of both distributed data mining and machine learning techniques, graph processing frameworks and large scale pattern mining approaches.

Includes members:
  Member "density based data partitioning strategy to approximate large scale subgraph mining":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "mr simlab scalable subgraph selection with label similarity for big data")
  Member "feature extraction in protein sequences classification a new stability measure":
    also connected through other hyperedges:
      (none)
  Member "densest subgraph in streaming and mapreduce":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "equivalence classes and conditional hardness in massively parallel computations")
  Member "graphlab a new framework for parallel machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "relaxed scheduling for scalable belief propagation")
  Member "k core decomposition of internet graphs hierarchies self similarity and measurement biases":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "building large k cores from sparse graphs")

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)