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 "an efficient homophilic model and algorithms for community detection using nash dynamics"):
  Abstract: The problem of community detection is important as it helps in understanding the spread of information in a social network. All real complex networks have an inbuilt structure which captures and characterizes the network dynamics between its nodes. Linkages are more likely to form between similar nodes, leading to the formation of some community structure which characterizes the network dynamic. The more friends they have in common, the more the influence that each person can exercise on the other. #R##N#We propose a disjoint community detection algorithm, $\textit{NashDisjoint}$ that detects disjoint communities in any given network. We evaluate the algorithm $\textit{NashDisjoint}$ on the standard LFR benchmarks, and we find that our algorithm works at least as good as that of the state of the art algorithms for the mixing factors less than 0.55 in all the cases. We propose an overlapping community detection algorithm $\textit{NashOverlap}$ to detect the overlapping communities in any given network. We evaluate the algorithm $\textit{NashOverlap}$ on the standard LFR benchmarks and we find that our algorithm works far better than the state of the art algorithms in around 152 different scenarios, generated by varying the number of nodes, mixing factor and overlapping membership. #R##N#We run our algorithm $\textit{NashOverlap}$ on DBLP dataset to detect the large collaboration groups and found very interesting results. Also, these results of our algorithm on DBLP collaboration network are compared with the results of the $\textit{COPRA}$ algorithm and $\textit{OSLOM}$.

Includes members:
  Member "overlapping community detection in networks the state of the art and comparative study":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "meta learning with relational information for short sequences")
  Member "virality prediction and community structure in social networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep collaborative embedding for information cascade prediction")
  Member "slpa uncovering overlapping communities in social networks via a speaker listener interaction dynamic process":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "self falsifiable hierarchical detection of overlapping communities on social networks")

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)