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: "random walks on simplicial complexes and the normalized hodge 1 laplacian"
  Abstract: Focusing on coupling between edges, we generalize the relationship between the normalized graph Laplacian and random walks on graphs by devising an appropriate normalization for the Hodge Laplacian -- the generalization of the graph Laplacian for simplicial complexes -- and relate this to a random walk on edges. Importantly, these random walks are intimately connected to the topology of the simplicial complex, just as random walks on graphs are related to the topology of the graph. This serves as a foundational step towards incorporating Laplacian-based analytics for higher-order interactions. We demonstrate how to use these dynamics for data analytics that extract information about the edge-space of a simplicial complex that complements and extends graph-based analysis. Specifically, we use our normalized Hodge Laplacian to derive spectral embeddings for examining trajectory data of ocean drifters near Madagascar and also develop a generalization of personalized PageRank for the edge-space of simplicial complexes to analyze a book co-purchasing dataset.

Connected through hyperedge HE-1 (induced by source paper "nonlinearity networks a 2020 vision"):
  with members:
    - "models of continuous time networks with tie decay diffusion and convection"
    - "tunable eigenvector based centralities for multiplex and temporal networks"
    - "architecture and evolution of semantic networks in mathematics texts"
    - "supracentrality analysis of temporal networks with directed interlayer coupling"
    - "using persistent homology to quantify a diurnal cycle in hurricane felix"
    - "persistent homology of geospatial data a case study with voting"
    - "network structure from rich but noisy data"
    - "the role of network analysis in industrial and applied mathematics"

Connected through hyperedge HE-2 (induced by source paper "graph based semi supervised active learning for edge flows"):
  with members:
    - "flow smoothing and denoising graph signal processing in the edge space"
    - "hodge laplacians on graphs"
    - "algorithms for lipschitz learning on graphs"
    - "active semi supervised learning using sampling theory for graph signals"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"
    - "active semi supervised learning using submodular functions"

Connected through hyperedge HE-3 (induced by source paper "flow smoothing and denoising graph signal processing in the edge space"):
  with members:
    - "simplicial closure and higher order link prediction"
    - "autoregressive moving average graph filtering"
    - "reconstruction of graph signals through percolation from seeding nodes"
    - "hierarchical clustering of asymmetric networks"
    - "sampling of graph signals with successive local aggregations"
    - "signals on graphs uncertainty principle and sampling"
    - "discrete signal processing on graphs sampling theory"
    - "a distributed tracking algorithm for reconstruction of graph signals"

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)