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 "recommending investors for new startups by integrating network diffusion and investors domain preference"):
  Abstract: Abstract   Over the past decade, many startups have sprung up, which create a huge demand for financial support from venture investors. However, due to the information asymmetry between investors and companies, the financing process is usually challenging and time-consuming, especially for the startups that have not yet obtained any investment. Because of this, effective data-driven techniques to automatically match startups with potentially relevant investors would be highly desirable. Here, we analyze 34,469 valid investment events collected from  www.itjuzi.com  and consider the cold-start problem of recommending investors for new startups. We address this problem by constructing different tripartite network representations of the data where nodes represent investors, companies, and companies’ domains. First, we find that investors have strong domain preferences when investing, which motivates us to introduce virtual links between investors and investment domains in the tripartite network construction. Our analysis of the recommendation performance of diffusion-based algorithms applied to various network representations indicates that prospective investors for new startups are effectively revealed by integrating network diffusion processes with investors’ domain preference.

Includes members:
  Member "inferring users preferences through leveraging their social relationships":
    also connected through other hyperedges:
      (none)
  Member "network based recommendation algorithms a review":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "enhancing countries fitness with recommender systems on the international trade network")
  Member "metadata embeddings for user and item cold start recommendations":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "predict your click out modeling user item interactions and session actions in an ensemble learning fashion")
  Member "recommending investors for crowdfunding projects":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "user donations in a crowdsourced video system")
  Member "collaborative filtering with diffusion based similarity on tripartite graphs":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "coarse cluster enhancing collaborative recommendation for social network systems")
  Member "personalized recommendation via integrated diffusion on user item tag tripartite graphs":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "coarse cluster enhancing collaborative recommendation for social network systems")

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.IR(Information Retrieval)