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 "discovering polarized communities in signed networks"):
  Abstract: Signed networks contain edge annotations to indicate whether each interaction is friendly (positive edge) or antagonistic (negative edge). The model is simple but powerful and it can capture novel and interesting structural properties of real-world phenomena. The analysis of signed networks has many applications from modeling discussions in social media, to mining user reviews, and to recommending products in e-commerce sites. In this paper we consider the problem of discovering polarized communities in signed networks. In particular, we search for two communities (subsets of the network vertices) where within communities there are mostly positive edges while across communities there are mostly negative edges. We formulate this novel problem as a "discrete eigenvector" problem, which we show to be NP-hard. We then develop two intuitive spectral algorithms: one deterministic, and one randomized with quality guarantee $\sqrt{n}$ (where $n$ is the number of vertices in the graph), tight up to constant factors. We validate our algorithms against non-trivial baselines on real-world signed networks. Our experiments confirm that our algorithms produce higher quality solutions, are much faster and can scale to much larger networks than the baselines, and are able to detect ground-truth polarized communities.

Includes members:
  Member "reducing controversy by connecting opposing views":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "vocabulary based method for quantifying controversy in social media")
  Member "signed link analysis in social media networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "social science guided feature engineering a novel approach to signed link analysis")
  Member "a survey of signed network mining in social media":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "social science guided feature engineering a novel approach to signed link analysis")
  Member "controversy and sentiment in online news":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "characterizing behavioral trends in a community driven discussion platform")
  Member "correlation clustering with constrained cluster sizes and extended weights bounds":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a parallel projection method for metric constrained optimization")
  Member "influence diffusion dynamics and influence maximization in social networks with friend and foe relationships":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "maximizing contrasting opinions in signed social networks")
  Member "the slashdot zoo mining a social network with negative edges":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deep network embedding for graph representation learning in signed networks")
  Member "correlation clustering with a fixed number of clusters":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "spectral clustering of signed graphs via matrix power means")

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.DS(Data Structures and Algorithms)