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: "nonlinearity networks a 2020 vision"
  Abstract: I briefly survey several fascinating topics in networks and nonlinearity. I highlight a few methods and ideas, including several of personal interest, that I anticipate to be especially important during the next several years. These topics include temporal networks (in which the entities and/or their interactions change in time), stochastic and deterministic dynamical processes on networks, adaptive networks (in which a dynamical process on a network is coupled to dynamics of network structure), and network structure and dynamics that include "higher-order" interactions (which involve three or more entities in a network). I draw examples from a variety of scenarios, including contagion dynamics, opinion models, waves, and coupled oscillators.

Connected through hyperedge HE-1 (induced by source paper "hypergraph cuts with general splitting functions"):
  with members:
    - "three hypergraph eigenvector centralities"
    - "simplicial closure and higher order link prediction"
    - "network flow based refinement for multilevel hypergraph partitioning"
    - "hypergraph partitioning for sparse matrix matrix multiplication"
    - "higher order organization of complex networks"
    - "a simple and strongly local flow based method for cut improvement"
    - "simple and fast rounding algorithms for directed and node weighted multiway cut"
    - "local distribution and the symmetry gap approximability of multiway partitioning problems"

Connected through hyperedge HE-2 (induced by source paper "migration networks applications of network analysis to large scale human mobility"):
  with members:
    - "social network analysis for social neuroscientists"
    - "persistent homology of geospatial data a case study with voting"
    - "think locally act locally detection of small medium sized and large communities in large networks"

Connected through hyperedge HE-3 (induced by source paper "clustering in graphs and hypergraphs with categorical edge labels"):
  with members:
    - "a framework for second order eigenvector centralities and clustering coefficients"
    - "solving linear programs in the current matrix multiplication time"
    - "three hypergraph eigenvector centralities"
    - "motif and hypergraph correlation clustering"
    - "simplicial closure and higher order link prediction"
    - "correlation clustering generalized"
    - "spectral sparsification of simplicial complexes for clustering and label propagation"
    - "inhomogoenous hypergraph clustering with applications"

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)