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 "multiscale dynamical embeddings of complex networks"):
  Abstract: Complex systems and relational data are often abstracted as dynamical processes on networks. To understand, predict and control their behavior a crucial step is to extract reduced descriptions of such networks. Inspired by notions from Control Theory, here we propose a time-dependent dynamical similarity measure between nodes, which quantifies the effect that a node input has on the network over time. This dynamical similarity induces an embedding that can be employed for several analysis tasks. Here we focus on (i) dimensionality reduction, by projecting nodes onto a low dimensional space capturing dynamic similarity at different time scales, and (ii) how to exploit our embeddings to uncover functional modules. We exemplify our ideas through case studies focusing on directed networks without strong connectivity, and signed networks. We further highlight how several ideas from community detection can be generalized in terms of our embedding perspective and linked to ideas from Control Theory.

Includes members:
  Member "the many facets of community detection in complex networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "embedding based silhouette community detection")
  Member "node2vec scalable feature learning for networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep graph matching consensus")
  Member "structure and inference in annotated networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on a scalable entropic breaching of the overfitting barrier in machine learning")
  Member "deepwalk online learning of social representations":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "memory based graph networks")
  Member "a generalized and adaptive method for community detection":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a mathematical theory for clustering in metric spaces")
  Member "ranking and clustering of nodes in networks with smart teleportation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "mapping higher order network flows in memory and multilayer networks with infomap")
  Member "a tutorial on spectral clustering":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "approximating spectral clustering via sampling a review")

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)