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: "a new framework for dynamical models on multiplex networks"
  Abstract: Many complex systems have natural representations as multi-layer networks. While these formulations retain more information than standard single-layer network models, there is not yet a fully developed theory for computing network metrics and statistics on these objects. We introduce a family of models of multiplex processes motivated by dynamical applications and investigate the properties of their spectra both theoretically and computationally. We study special cases of multiplex diffusion and Markov dynamics, using the spectral results to compute their rates of convergence. We use our framework to define a version of multiplex eigenvector centrality, which generalizes some existing notions in the literature. Last, we compare our operator to structurally-derived models on synthetic and real-world networks, helping delineate the contexts in which the different frameworks are appropriate.

Connected through hyperedge HE-1 (induced by source paper "supracentrality analysis of temporal networks with directed interlayer coupling"):
  with members:
    - "tunable eigenvector based centralities for multiplex and temporal networks"
    - "relating modularity maximization and stochastic block models in multilayer networks"
    - "a new framework for centrality measures in multiplex networks"
    - "distributed algorithms for computation of centrality measures in complex networks"
    - "node and layer eigenvector centralities for multiplex networks"
    - "post processing partitions to identify domains of modularity optimization"
    - "block matrix formulations for evolving networks"
    - "pagerank beyond the web"

Connected through hyperedge HE-2 (induced by source paper "multiplex communities and the emergence of international conflict"):
  with members:
    - "identification interpretability and bayesian word embeddings"
    - "hiding individuals and communities in a social network"
    - "understanding state preferences with text as data introducing the un general debate corpus"
    - "the ground truth about metadata and community detection in networks"
    - "higher order organization of complex networks"
    - "learning scalable deep kernels with recurrent structure"
    - "community extraction in multilayer networks with heterogeneous community structure"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-3 (induced by source paper "spectral clustering methods for multiplex networks"):
  with members:
    - "geometric multiscale community detection markov stability and vector partitioning"
    - "community detection link prediction and layer interdependence in multilayer networks"
    - "detectability of small communities in multilayer and temporal networks eigenvector localization layer aggregation and time series discretization"
    - "non negative matrix factorizations for multiplex network analysis"
    - "generative benchmark models for mesoscale structures in multilayer networks"
    - "general tensor spectral co clustering for higher order data"
    - "network models that reflect multiplex dynamics"
    - "a local perspective on community structure in multilayer networks"

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)