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: "ricci curvature of the internet topology"
  Abstract: Analysis of Internet topologies has shown that the Internet topology has negative curvature, measured by Gromov's "thin triangle condition", which is tightly related to core congestion and route reliability. In this work we analyze the discrete Ricci curvature of the Internet, defined by Ollivier, Lin, etc. Ricci curvature measures whether local distances diverge or converge. It is a more local measure which allows us to understand the distribution of curvatures in the network. We show by various Internet data sets that the distribution of Ricci cuvature is spread out, suggesting the network topology to be non-homogenous. We also show that the Ricci curvature has interesting connections to both local measures such as node degree and clustering coefficient, global measures such as betweenness centrality and network connectivity, as well as auxilary attributes such as geographical distances. These observations add to the richness of geometric structures in complex network theory.

Connected through hyperedge HE-1 (induced by source paper "computationally tractable riemannian manifolds for graph embeddings"):
  with members:
    - "riemannian adaptive optimization methods"
    - "learning continuous hierarchies in the lorentz model of hyperbolic geometry"
    - "grassmannian learning embedding geometry awareness in shallow and deep learning"
    - "hyperbolic entailment cones for learning hierarchical embeddings"
    - "poincar e embeddings for learning hierarchical representations"
    - "node2vec scalable feature learning for networks"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "complex embeddings for simple link prediction"

Connected through hyperedge HE-2 (induced by source paper "understanding the power of persistence pairing via permutation test"):
  with members:
    - "learning metrics for persistence based summaries and applications for graph classification"
    - "on the metric distortion of embedding persistence diagrams into separable hilbert spaces"
    - "a simple yet effective baseline for non attributed graph classification"
    - "deep sets"
    - "on the metric distortion of embedding persistence diagrams into reproducing kernel hilbert spaces"
    - "sliced wasserstein kernel for persistence diagrams"
    - "deep learning with topological signatures"
    - "3d shapenets a deep representation for volumetric shapes"

Connected through hyperedge HE-3 (induced by source paper "it means more if it sounds good yet another hypotheses concerning the evolution of polysemous words"):
  with members:
    - "community detection on networks with ricci flow"

Connected through hyperedge HE-4 (induced by source paper "community detection on networks with ricci flow"):
  with members:
    - "network alignment by discrete ollivier ricci flow"
    - "the ground truth about metadata and community detection in networks"
    - "characterizing complex networks with forman ricci curvature and associated geometric flows"
    - "equivalence between modularity optimization and maximum likelihood methods for community detection"
    - "ricci ollivier curvature of the rooted phylogenetic subtree prune regraft graph"
    - "empirical comparison of algorithms for network community detection"
    - "objective criteria for the evaluation of clustering methods"

Connected through hyperedge HE-5 (induced by source paper "measuring road network topology vulnerability by ricci curvature"):
  with members:
    - "discriminative distance based network indices with application to link prediction"
    - "characterizing complex networks with forman ricci curvature and associated geometric flows"

Connected through hyperedge HE-6 (induced by source paper "network alignment by discrete ollivier ricci flow"):
  with members:
    - "node2vec scalable feature learning for networks"
    - "graph isomorphism in quasipolynomial time"
    - "ricci ollivier curvature of the rooted phylogenetic subtree prune regraft graph"
    - "on the reliability of profile matching across large online social networks"
    - "deepwalk online learning of social representations"
    - "practical graph isomorphism ii"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-7 (induced by source paper "curvature of hypergraphs via multi marginal optimal transport"):
  with members:
    - "relaxation based coarsening for multilevel hypergraph partitioning"
    - "higher order organization of complex networks"
    - "spectral properties of laplacian and stochastic diffusion process for edge expansion in hypergraphs"
    - "hypergraph markov operators eigenvalues and approximation algorithms"

Connected through hyperedge HE-8 (induced by source paper "characterizing complex networks with forman ricci curvature and associated geometric flows"):
  with members:
    - "forman ricci flow for change detection in large dynamic data sets"
    - "discovering social circles in ego networks"
    - "mapping the gnutella network properties of large scale peer to peer systems and implications for system design"

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)