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: "i know you ll be back interpretable new user clustering and churn prediction on a mobile social application"
  Abstract: As online platforms are striving to get more users, a critical challenge is user churn, which is especially concerning for new users. In this paper, by taking the anonymous large-scale real-world data from Snapchat as an example, we develop \textit{ClusChurn}, a systematic two-step framework for interpretable new user clustering and churn prediction, based on the intuition that proper user clustering can help understand and predict user churn. Therefore, \textit{ClusChurn} firstly groups new users into interpretable typical clusters, based on their activities on the platform and ego-network structures. Then we design a novel deep learning pipeline based on LSTM and attention to accurately predict user churn with very limited initial behavior data, by leveraging the correlations among users' multi-dimensional activities and the underlying user types. \textit{ClusChurn} is also able to predict user types, which enables rapid reactions to different types of user churn. Extensive data analysis and experiments show that \textit{ClusChurn} provides valuable insight into user behaviors, and achieves state-of-the-art churn prediction performance. The whole framework is deployed as a data analysis pipeline, delivering real-time data analysis and prediction results to multiple relevant teams for business intelligence uses. It is also general enough to be readily adopted by any online systems with user behavior data.

Connected through hyperedge HE-1 (induced by source paper "relation learning on social networks with multi modal graph edge variational autoencoders"):
  with members:
    - "similarity modeling on heterogeneous networks via automatic path discovery"
    - "neural embedding propagation on heterogeneous networks"
    - "cubenet multi facet hierarchical heterogeneous network construction analysis and mining"
    - "representation learning on graphs with jumping knowledge networks"
    - "fastgcn fast learning with graph convolutional networks via importance sampling"
    - "relationship profiling over social networks reverse smoothness from similarity to closeness"
    - "metapad meta pattern discovery from massive text corpora"
    - "topological recurrent neural network for diffusion prediction"

Connected through hyperedge HE-2 (induced by source paper "model agnostic linear competitors when interpretable models compete and collaborate with black box models"):
  with members:
    - "fairwashing the risk of rationalization"
    - "the dangers of post hoc interpretability unjustified counterfactual explanations"
    - "an interpretable model with globally consistent explanations for credit risk"
    - "on the robustness of interpretability methods"
    - "interpretable explorable approximations of black box models"
    - "a unified approach to interpreting model predictions"
    - "right for the right reasons training differentiable models by constraining their explanations"
    - "harnessing deep neural networks with logic rules"

Connected through hyperedge HE-3 (induced by source paper "characterizing and forecasting user engagement with in app action graph a case study of snapchat"):
  with members:
    - "i ll be back on the multiple lives of users of a mobile activity tracking application"
    - "semi supervised classification with graph convolutional networks"
    - "donor retention in online crowdfunding communities a case study of donorschoose org"
    - "moodbar increasing new user retention in wikipedia through lightweight socialization"
    - "user session identification based on strong regularities in inter activity time"

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)