human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "prediction of esg compliance using a heterogeneous information network"):
  Abstract: Negative screening is one method to avoid interactions with inappropriate entities. For example, financial institutions keep investment exclusion lists of inappropriate firms that have environmental, social, and government (ESG) problems. They create their investment exclusion lists by gathering information from various news sources to keep their portfolios profitable as well as green. International organizations also maintain smart sanctions lists that are used to prohibit trade with entities that are involved in illegal activities. In the present paper, we focus on the prediction of investment exclusion lists in the finance domain. We construct a vast heterogeneous information network that covers the necessary information surrounding each firm, which is assembled using seven professionally curated datasets and two open datasets, which results in approximately 50 million nodes and 400 million edges in total. Exploiting these vast datasets and motivated by how professional investigators and journalists undertake their daily investigations, we propose a model that can learn to predict firms that are more likely to be added to an investment exclusion list in the near future. Our approach is tested using the negative news investment exclusion list data of more than 35,000 firms worldwide from January 2012 to May 2018. Comparing with the state-of-the-art methods with and without using the network, we show that the predictive accuracy is substantially improved when using the vast information stored in the heterogeneous information network. This work suggests new ways to consolidate the diffuse information contained in big data to monitor dominant firms on a global scale for better risk management and more socially responsible investment.

Includes members:
  Member "ripplenet propagating user preferences on the knowledge graph for recommender systems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a survey on knowledge graph based recommender systems")
  Member "reinforcement learning to rank in e commerce search engine formalization analysis and application":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "online similarity learning with feedback for invoice line item matching")
  Member "hitfraud a broad learning approach for collective fraud detection in heterogeneous information networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "predicting adverse media risk using a heterogeneous information network")
  Member "a survey of heterogeneous information network analysis":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "prince provider side interpretability with counterfactual explanations in recommender systems")
  Member "supervised random walks predicting and recommending links in social networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "few shot learning on graphs via super classes based on graph spectral measures")

Question: Please tell me which class the source paper 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)