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 "role discovery in networks"):
  Abstract: Roles represent node-level connectivity patterns such as star-center, star-edge nodes, near-cliques or nodes that act as bridges to different regions of the graph. Intuitively, two nodes belong to the same role if they are structurally similar. Roles have been mainly of interest to sociologists, but more recently, roles have become increasingly useful in other domains. Traditionally, the notion of roles were defined based on graph equivalences such as structural, regular, and stochastic equivalences. We briefly revisit these early notions and instead propose a more general formulation of roles based on the similarity of a feature representation (in contrast to the graph representation). This leads us to propose a taxonomy of three general classes of techniques for discovering roles that includes(i) graph-based roles, (ii) feature-based roles, and (iii) hybrid roles. We also propose a flexible framework for discovering roles using the notion of similarity on a feature-based representation. The framework consists of two fundamental components: (a) role featureconstruction and (b) role assignment using the learned feature representation. We discuss the different possibilities for discoveringfeature-based roles and the tradeoffs of the many techniques for computing them. Finally, we discuss potential applications and future directions and challenges.

Includes members:
  Member "a boosting approach to learning graph representations":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "locally boosted graph aggregation for community detection")
  Member "an information theoretic analysis of hard and soft assignment methods for clustering":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "probabilistic k means clustering via nonlinear programming")
  Member "does a daily deal promotion signal a distressed business an empirical investigation of small business survival":
    also connected through other hyperedges:
      (none)
  Member "role dynamics fast mining of large dynamic networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "temporal network representation learning")
  Member "network sampling from static to streaming graphs":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "understanding the limitations of network online learning")
  Member "correlated non parametric latent feature models":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "weakly supervised image annotation and segmentation with objects and attributes")
  Member "on learning discrete graphical models using greedy methods":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "privately learning markov random fields")
  Member "the internet as level topology three data sources and one definitive metric":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "the graph structure of the internet at the autonomous systems level during ten years")

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)