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: "role dynamics fast mining of large dynamic networks"
  Abstract: To understand the structural dynamics of a large-scale social, biological or technological network, it may be useful to discover behavioral roles representing the main connectivity patterns present over time. In this paper, we propose a scalable non-parametric approach to automatically learn the structural dynamics of the network and individual nodes. Roles may represent structural or behavioral patterns such as the center of a star, peripheral nodes, or bridge nodes that connect different communities. Our novel approach learns the appropriate structural role dynamics for any arbitrary network and tracks the changes over time. In particular, we uncover the specific global network dynamics and the local node dynamics of a technological, communication, and social network. We identify interesting node and network patterns such as stationary and non-stationary roles, spikes/steps in role-memberships (perhaps indicating anomalies), increasing/decreasing role trends, among many others. Our results indicate that the nodes in each of these networks have distinct connectivity patterns that are non-stationary and evolve considerably over time. Overall, the experiments demonstrate the effectiveness of our approach for fast mining and tracking of the dynamics in large networks. Furthermore, the dynamic structural representation provides a basis for building more sophisticated models and tools that are fast for exploring large dynamic networks.

Connected through hyperedge HE-1 (induced by source paper "temporal network representation learning"):
  with members:
    - "heterogeneous network motifs"
    - "node2bits compact time and attribute aware node representations for user stitching"
    - "dylink2vec effective feature representation for link prediction in dynamic networks"
    - "models for capturing temporal smoothness in evolving networks for learning latent representation of nodes"
    - "deep generative dual memory network for continual learning"
    - "attributed social network embedding"
    - "struc2vec learning node representations from structural identity"
    - "psdvec a toolbox for incremental and scalable word embedding"

Connected through hyperedge HE-2 (induced by source paper "exploring the evolution of node neighborhoods in dynamic networks"):
  with members:
    - "interpreting communities based on the evolution of a dynamic attributed network"
    - "a method for characterizing communities in dynamic attributed complex networks"
    - "centrality metric for dynamic networks"

Connected through hyperedge HE-3 (induced by source paper "graph summarization methods and applications a survey"):
  with members:
    - "struc2vec learning node representations from structural identity"
    - "by chance is not enough preserving relative density through non uniform sampling"
    - "node2vec scalable feature learning for networks"
    - "compressing graphs and indexes with recursive graph bisection"
    - "network structure inference a survey motivations methods and applications"
    - "efficient frequent directions algorithm for sparse matrices"
    - "pnp fast path ensemble method for movie design"
    - "an empirical comparison of the summarization power of graph clustering methods"

Connected through hyperedge HE-4 (induced by source paper "role discovery in networks"):
  with members:
    - "a boosting approach to learning graph representations"
    - "an information theoretic analysis of hard and soft assignment methods for clustering"
    - "does a daily deal promotion signal a distressed business an empirical investigation of small business survival"
    - "network sampling from static to streaming graphs"
    - "correlated non parametric latent feature models"
    - "on learning discrete graphical models using greedy methods"
    - "the internet as level topology three data sources and one definitive metric"

Connected through hyperedge HE-5 (induced by source paper "graph based anomaly detection and description a survey"):
  with members:
    - "detecting change points in the large scale structure of evolving networks"
    - "deltacon a principled massive graph similarity function"
    - "discriminative probabilistic models for relational data"
    - "estimating the prevalence of deception in online review communities"
    - "identifying the signs of fraudulent accounts using data mining techniques"
    - "netsimile a scalable approach to size independent network similarity"
    - "measuring tie strength in implicit social networks"
    - "finding deceptive opinion spam by any stretch of the imagination"

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)