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: "event organization 101 understanding latent factors of event popularity"
  Abstract: The problem of understanding people's participation in real-world events has been a subject of active research and can offer valuable insights for human behavior analysis and event-related recommendation/advertisement. The emergence of event-based social networks (EBSNs), which attracts online users for hosting/attending offline events, has enabled exciting new research in this domain. However, most existing works focus on understanding or predicting/recommending individual users' event participation. Few study has addressed the problem of event popularity (i.e., number of attendees) from the event organizer's point of view. #R##N#In this work, we study the latent factors for determining event popularity using large-scale datasets collected from the popular Meetup.com EBSN in five major cities around the world. We have conducted modeling analysis of four contextual factors (spatial, group, temporal, and semantic), and also developed a group-based social influence propagation network to model group-specific influences on events. By combining the Contextual features And Social Influence NetwOrk, our integrated prediction framework CASINO can capture the diverse influential factors of event participation and can be used by event organizers to predict/improve the popularity of their events. Evaluations demonstrate that our CASINO framework achieves high prediction accuracy with contributions from all the latent features we capture.

Connected through hyperedge HE-1 (induced by source paper "predicting event attendance exploring social influence"):
  with members:
    - "exploring the semantic content of unsupervised graph embeddings an empirical study"
    - "hyperbolic entailment cones for learning hierarchical embeddings"
    - "harp hierarchical representation learning for networks"
    - "poincar e embeddings for learning hierarchical representations"
    - "node2vec scalable feature learning for networks"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "understanding group event scheduling via the outwithfriendz mobile application"):
  with members:
    - "group colocation behavior in technological social networks"
    - "a data based approach to social influence maximization"
    - "influence diffusion dynamics and influence maximization in social networks with friend and foe relationships"

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)