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 "scalable label propagation algorithms for heterogeneous networks"):
  Abstract: Heterogeneous networks are large graphs consisting of different types of nodes and edges. They are an important category of complex networks, but the process of knowledge extraction and relations discovery from these networks are so complicated and time-consuming. Moreover, the scale of these networks is steadily increasing. Thus, scalable and accurate methods are required for efficient knowledge extraction. In this paper, two distributed label propagation algorithms, namely DHLP-1 and DHLP-2, in the heterogeneous networks have been introduced. The Apache Giraph platform is employed which provides a vertex-centric programming model for designing and running distributed graph algorithms. Complex heterogeneous networks have many examples in the real world and are widely used today for modeling complicated processes. Biological networks are one of such networks. As a case study, we have measured the efficiency of our proposed DHLP-1 and DHLP-2 algorithms on a biological network consisting of drugs, diseases, and targets. The subject we have studied in this network is drug repositioning, aimed at saving both time and cost by suggesting new indications for the current drugs. We compared the proposed algorithms with similar non-distributed versions of them namely MINProp and Heter-LP. The experiments revealed that the runtime of the algorithms has decreased in the distributed versions rather than non-distributed ones dramatically. The effectiveness of our proposed algorithms against other algorithms is supported through statistical analysis of 10-fold cross-validation as well as experimental analysis.

Includes members:
  Member "a survey of heterogeneous information network analysis":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "prince provider side interpretability with counterfactual explanations in recommender systems")
  Member "labelrank a stabilized label propagation algorithm for community detection in networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "discovering communities of community discovery")
  Member "parallel processing of large graphs":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "actors vs shared memory two models at work on big data application frameworks")

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)