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: "forman ricci flow for change detection in large dynamic data sets"
  Abstract: We present a viable solution to the challenging question of change detection in complex networks inferred from large dynamic data sets. Building on Forman's discretization of the classical notion of Ricci curvature, we introduce a novel geometric method to characterize different types of real-world networks with an emphasis on peer-to-peer networks. Furthermore we adapt the classical Ricci flow that already proved to be a powerful tool in image processing and graphics, to the case of undirected and weighted networks. The application of the proposed method on peer-to-peer networks yields insights into topological properties and the structure of their underlying data.

Connected through hyperedge HE-1 (induced by source paper "why did the shape of your network change on detecting network anomalies via non local curvatures"):
  with members:
    - "fast approximation and exact computation of negative curvature parameters of graphs"
    - "effect of gromov hyperbolicity parameter on cuts and expansions in graphs and some algorithmic implications"
    - "computing the gromov hyperbolicity of a discrete metric space"
    - "euclidean versus hyperbolic congestion in idealized versus experimental networks"

Connected through hyperedge HE-2 (induced by source paper "forman s ricci curvature from networks to hypernetworks"):
  with members:
    - "curvature of hypergraphs via multi marginal optimal transport"
    - "structural deep embedding for hyper networks"
    - "characterizing complex networks with forman ricci curvature and associated geometric flows"

Connected through hyperedge HE-3 (induced by source paper "characterizing complex networks with forman ricci curvature and associated geometric flows"):
  with members:
    - "ricci curvature of the internet topology"
    - "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)