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: "recommending investors for crowdfunding projects"
  Abstract: To bring their innovative ideas to market, those embarking in new ventures have to raise money, and, to do so, they have often resorted to banks and venture capitalists. Nowadays, they have an additional option: that of crowdfunding. The name refers to the idea that funds come from a network of people on the Internet who are passionate about supporting others' projects. One of the most popular crowdfunding sites is Kickstarter. In it, creators post descriptions of their projects and advertise them on social media sites (mainly Twitter), while investors look for projects to support. The most common reason for project failure is the inability of founders to connect with a sufficient number of investors, and that is mainly because hitherto there has not been any automatic way of matching creators and investors. We thus set out to propose different ways of recommending investors found on Twitter for specific Kickstarter projects. We do so by conducting hypothesis-driven analyses of pledging behavior and translate the corresponding findings into different recommendation strategies. The best strategy achieves, on average, 84% of accuracy in predicting a list of potential investors' Twitter accounts for any given project. Our findings also produced key insights about the whys and wherefores of investors deciding to support innovative efforts.

Connected through hyperedge HE-1 (induced by source paper "recommending investors for new startups by integrating network diffusion and investors domain preference"):
  with members:
    - "inferring users preferences through leveraging their social relationships"
    - "network based recommendation algorithms a review"
    - "metadata embeddings for user and item cold start recommendations"
    - "collaborative filtering with diffusion based similarity on tripartite graphs"
    - "personalized recommendation via integrated diffusion on user item tag tripartite graphs"

Connected through hyperedge HE-2 (induced by source paper "user donations in a crowdsourced video system"):
  with members:
    - "representation learning on graphs methods and applications"
    - "understanding video ad consumption on youtube a measurement study on user behavior popularity and content properties"
    - "node2vec scalable feature learning for networks"
    - "donor retention in online crowdfunding communities a case study of donorschoose org"
    - "line large scale information network embedding"
    - "deepwalk online learning of social representations"

Connected through hyperedge HE-3 (induced by source paper "identifying on time reward delivery projects with estimating delivery duration on kickstarter"):
  with members:
    - "how to succeed in crowdfunding a long term study in kickstarter"
    - "donor retention in online crowdfunding communities a case study of donorschoose org"
    - "who will retweet this automatically identifying and engaging strangers on twitter to spread information"

Connected through hyperedge HE-4 (induced by source paper "how to succeed in crowdfunding a long term study in kickstarter"):
  with members:
    - "donor retention in online crowdfunding communities a case study of donorschoose org"

Connected through hyperedge HE-5 (induced by source paper "who contributes to the knowledge sharing economy"):
  with members:
    - "uncovering the temporal dynamics of diffusion 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)