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: "leveraging network dynamics for improved link prediction"
  Abstract: The aim of link prediction is to forecast connections that are most likely to occur in the future, based on examples of previously observed links. A key insight is that it is useful to explicitly model network dynamics, how frequently links are created or destroyed when doing link prediction. In this paper, we introduce a new supervised link prediction framework, RPM (Rate Prediction Model). In addition to network similarity measures, RPM uses the predicted rate of link modifications, modeled using time series data; it is implemented in Spark-ML and trained with the original link distribution, rather than a small balanced subset. We compare the use of this network dynamics model to directly creating time series of network similarity measures. Our experiments show that RPM, which leverages predicted rates, outperforms the use of network similarity measures, either individually or within a time series.

Connected through hyperedge HE-1 (induced by source paper "investigating negative interactions in multiplex networks a mutual information approach"):
  with members:
    - "signed link analysis in social media networks"
    - "a multilayer approach to multiplexity and link prediction in online geo social networks"
    - "link prediction in social networks the state of the art"
    - "link prediction in complex networks a mutual information perspective"
    - "applying weighted pagerank to author citation networks"
    - "the slashdot zoo mining a social network with negative edges"

Connected through hyperedge HE-2 (induced by source paper "continuous time relationship prediction in dynamic heterogeneous information networks"):
  with members:
    - "predicting anchor links between heterogeneous social networks"
    - "a survey of heterogeneous information network analysis"
    - "semi supervised sequence learning"
    - "link prediction in social networks the state of the art"
    - "adadelta an adaptive learning rate method"

Connected through hyperedge HE-3 (induced by source paper "a holistic approach for predicting links in coevolving multilayer networks"):
  with members:
    - "a multilayer approach to multiplexity and link prediction in online geo social networks"
    - "rankmerging a supervised learning to rank framework to predict links in large social network"
    - "applying weighted pagerank to author citation networks"

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)