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: "detecting change points in the large scale structure of evolving networks"
  Abstract: Interactions among people or objects are often dynamic in nature and can be represented as a sequence of networks, each providing a snapshot of the interactions over a brief period of time. An important task in analyzing such evolving networks is change-point detection, in which we both identify the times at which the large-scale pattern of interactions changes fundamentally and quantify how large and what kind of change occurred. Here, we formalize for the first time the network change-point detection problem within an online probabilistic learning framework and introduce a method that can reliably solve it. This method combines a generalized hierarchical random graph model with a Bayesian hypothesis test to quantitatively determine if, when, and precisely how a change point has occurred. We analyze the detectability of our method using synthetic data with known change points of different types and magnitudes, and show that this method is more accurate than several previously used alternatives. Applied to two high-resolution evolving social networks, this method identifies a sequence of change points that align with known external "shocks" to these networks.

Connected through hyperedge HE-1 (induced by source paper "change detection in dynamic attributed networks"):
  with members:
    - "anomaly detection in online social networks"
    - "interpreting communities based on the evolution of a dynamic attributed network"
    - "link prediction in networks with nodes attributes by similarity propagation"
    - "graph based anomaly detection and description a survey"
    - "large scale spectral clustering using resistance distance and spielman teng solvers"

Connected through hyperedge HE-2 (induced by source paper "community detection in networks without observing edges"):
  with members:
    - "the ground truth about metadata and community detection in networks"

Connected through hyperedge HE-3 (induced by source paper "change detection in noisy dynamic networks a spectral embedding approach"):
  with members:
    - "change detection in dynamic attributed networks"
    - "a probabilistic framework to node level anomaly detection in communication networks"
    - "dyngem deep embedding method for dynamic graphs"
    - "link prediction for interdisciplinary collaboration via co authorship network"
    - "hellrank a hellinger based centrality measure for bipartite social networks"
    - "fast change point detection on dynamic social networks"
    - "detection and localization of change points in temporal networks with the aid of stochastic block models"
    - "localization and centrality in networks"

Connected through hyperedge HE-4 (induced by source paper "challenges in community discovery on temporal networks"):
  with members:
    - "olcpm an online framework for detecting overlapping communities in dynamic social networks"
    - "community discovery in dynamic networks a survey"
    - "stream graphs and link streams for the modeling of interactions over time"
    - "when is a network a network multi order graphical model selection in pathways and temporal networks"
    - "the ground truth about metadata and community detection in networks"
    - "a framework for the construction of generative models for mesoscale structure in multilayer networks"
    - "snap a general purpose network analysis and graph mining library"
    - "generative benchmark models for mesoscale structures in multilayer networks"

Connected through hyperedge HE-5 (induced by source paper "detecting stable communities in link streams at multiple temporal scales"):
  with members:
    - "detecting sequences of system states in temporal networks"
    - "olcpm an online framework for detecting overlapping communities in dynamic social networks"
    - "community discovery in dynamic networks a survey"
    - "motifs in temporal networks"
    - "stream graphs and link streams for the modeling of interactions over time"
    - "fast change point detection on dynamic social networks"
    - "community structure in large networks natural cluster sizes and the absence of large well defined clusters"

Connected through hyperedge HE-6 (induced by source paper "detecting sequences of system states in temporal networks"):
  with members:
    - "community discovery in dynamic networks a survey"
    - "concept drift and anomaly detection in graph streams"
    - "fast change point detection on dynamic social networks"
    - "measuring large scale social networks with high resolution"
    - "graph based anomaly detection and description a survey"
    - "modeling complex systems with adaptive networks"

Connected through hyperedge HE-7 (induced by source paper "unsupervised network embedding for graph visualization clustering and classification"):
  with members:
    - "detecting sequences of system states in temporal networks"
    - "multi hop assortativities for network classification"
    - "fine grained attention mechanism for neural machine translation"
    - "graph embedding techniques applications and performance a survey"
    - "a comprehensive survey of graph embedding problems techniques and applications"
    - "feature based classification of networks"

Connected through hyperedge HE-8 (induced by source paper "constant state of change engagement inequality in temporal dynamic networks"):
  with members:
    - "motifs in temporal networks"
    - "predicting user roles in social networks using transfer learning with feature transformation"
    - "role discovery in networks"
    - "inferring networks of diffusion and influence"

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)