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: "the lifecycle and cascade of wechat social messaging groups"
  Abstract: Social instant messaging services are emerging as a transformative form with which people connect, communicate with friends in their daily life - they catalyze the formation of social groups, and they bring people stronger sense of community and connection. However, research community still knows little about the formation and evolution of groups in the context of social messaging - their lifecycles, the change in their underlying structures over time, and the diffusion processes by which they develop new members. In this paper, we analyze the daily usage logs from WeChat group messaging platform - the largest standalone messaging communication service in China - with the goal of understanding the processes by which social messaging groups come together, grow new members, and evolve over time. Specifically, we discover a strong dichotomy among groups in terms of their lifecycle, and develop a separability model by taking into account a broad range of group-level features, showing that long-term and short-term groups are inherently distinct. We also found that the lifecycle of messaging groups is largely dependent on their social roles and functions in users' daily social experiences and specific purposes. Given the strong separability between the long-term and short-term groups, we further address the problem concerning the early prediction of successful communities. In addition to modeling the growth and evolution from group-level perspective, we investigate the individual-level attributes of group members and study the diffusion process by which groups gain new members. By considering members' historical engagement behavior as well as the local social network structure that they embedded in, we develop a membership cascade model and demonstrate the effectiveness by achieving AUC of 95.31% in predicting inviter, and an AUC of 98.66% in predicting invitee.

Connected through hyperedge HE-1 (induced by source paper "vulnds top k vulnerable sme detection system in networked loans"):
  with members:
    - "an in depth comparison of s t reliability algorithms over uncertain graphs"
    - "joint deep modeling of users and items using reviews for recommendation"
    - "wide deep learning for recommender systems"

Connected through hyperedge HE-2 (induced by source paper "deepinf social influence prediction with deep learning"):
  with members:
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "inductive representation learning on large graphs"
    - "attention is all you need"
    - "a century of science globalization of scientific collaborations citations and innovations"
    - "representation learning on graphs methods and applications"
    - "instance normalization the missing ingredient for fast stylization"
    - "structural diversity and homophily a study across more than one hundred big networks"
    - "node2vec scalable feature learning for networks"

Connected through hyperedge HE-3 (induced by source paper "weseer visual analysis for better information cascade prediction of wechat articles"):
  with members:
    - "modeling and predicting popularity dynamics of microblogs using self excited hawkes processes"
    - "seismic a self exciting point process model for predicting tweet popularity"
    - "can cascades be predicted"
    - "popularity prediction in microblogging network a case study on sina weibo"
    - "cumulative effect in information diffusion empirical study on a microblogging network"
    - "the pulse of news in social media forecasting popularity"
    - "structure and dynamics of information pathways in online media"

Connected through hyperedge HE-4 (induced by source paper "a selfie is worth a thousand words mining personal patterns behind user selfie posting behaviours"):
  with members:
    - "what the language you tweet says about your occupation"
    - "on diffusion restricted social network a measurement study of wechat moments"
    - "deep residual learning for image recognition"
    - "identifying relevant messages in a twitter based citizen channel for natural disaster situations"
    - "the digital life of walkable streets"
    - "a picture tells a thousand words about you user interest profiling from user generated visual content"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-5 (induced by source paper "visual analytics for loan guarantee network risk management"):
  with members:
    - "anomaly detection in online social networks"
    - "xgboost a scalable tree boosting system"
    - "technical report graph structured sparse optimization for connected subgraph detection"
    - "sales pipeline win propensity prediction a regression approach"
    - "general optimization technique for high quality community detection in complex networks"

Connected through hyperedge HE-6 (induced by source paper "predicting the evolution of complex networks via similarity dynamics"):
  with members:
    - "full scale cascade dynamics prediction with a local first approach"
    - "ultra accurate collaborative information filtering via directed user similarity"
    - "can cascades be predicted"
    - "integrated structure investigation in complex networks by label propagation"
    - "the rich club phenomenon in the internet topology"

Connected through hyperedge HE-7 (induced by source paper "a hybrid approach for risk assessment of loan guarantee network"):
  with members:
    - "modeling the intensity function of point process via recurrent neural networks"
    - "xgboost a scalable tree boosting system"
    - "sales pipeline win propensity prediction a regression approach"
    - "k core decomposition a tool for the visualization of large scale 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)