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 "towards realistic artificial benchmark for community detection algorithms evaluation"):
  Abstract: Many algorithms have been proposed for revealing the community structure in complex networks. Tests under a wide range of realistic conditions must be performed in order to select the most appropriate for a particular application. Artificially generated networks are often used for this purpose. The most realistic generative method to date has been proposed by Lancichinetti, Fortunato and Radicchi LFR. However, it does not produce networks with some typical features of real-world networks. To overcome this drawback, we investigate two alternative modifications of this algorithm. Experimental results show that in both cases, centralisation and degree correlation values of generated networks are closer to those encountered in real-world networks. The three benchmarks have been used on a wide set of prominent community detection algorithms in order to reveal the limits and the robustness of the algorithms. Results show that the detection of meaningful communities gets harder with more realistic networks, and particularly when the proportion of inter-community links increases.

Includes members:
  Member "an empirical study of the relation between community structure and transitivity":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "movienet a movie multilayer network model using visual and textual semantic cues")
  Member "qualitative comparison of community detection algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a general framework for complex network based image segmentation")
  Member "on accuracy of community structure discovery algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a general framework for complex network based image segmentation")

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)