human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "semi supervised classification on graphs using explicit diffusion dynamics"):
  with members:
    - "quantifying the alignment of graph and features in deep learning"
    - "graph based data clustering via multiscale community detection"
    - "geometric multiscale community detection markov stability and vector partitioning"
    - "geniepath graph neural networks with adaptive receptive paths"
    - "gaan gated attention networks for learning on large and spatiotemporal graphs"
    - "large scale learnable graph convolutional networks"
    - "geometric deep learning going beyond euclidean data"
    - "cayleynets graph convolutional neural networks with complex rational spectral filters"

Connected through hyperedge HE-2 (induced by source paper "graph based data clustering via multiscale community detection"):
  with members:
    - "from free text to clusters of content in health records an unsupervised graph partitioning approach"
    - "scale variant topological information for characterizing the structure of complex networks"
    - "geometric deep learning going beyond euclidean data"
    - "finding role communities in directed networks using role based similarity markov stability and the relaxed minimum spanning tree"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-3 (induced by source paper "structured networks and coarse grained descriptions a dynamical perspective"):
  with members:
    - "a tutorial on modeling and analysis of dynamic social networks part i"
    - "the many facets of community detection in complex networks"
    - "the ground truth about metadata and community detection in networks"
    - "opinion dynamics in social networks with hostile camps consensus vs polarization"
    - "seismic a self exciting point process model for predicting tweet popularity"
    - "spectra of random graphs with community structure and arbitrary degrees"
    - "spectral redemption in clustering sparse networks"
    - "opinion fluctuations and disagreement in social networks"

Connected through hyperedge HE-4 (induced by source paper "guided graph spectral embedding application to the c elegans connectome"):
  with members:
    - "when slepian meets fiedler putting a focus on the graph spectrum"
    - "steering macro scale network community structure by micro scale features"
    - "signals on graphs uncertainty principle and sampling"
    - "a spectral graph uncertainty principle"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"
    - "discrete signal processing on graphs"
    - "a tutorial on spectral clustering"

Question: Please tell me which class the center node 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)