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: "trend prediction in temporal bipartite networks the case of movielens netflix and digg"
  Abstract: Online systems, where users purchase or collect items of some kind, can be effectively represented by temporal bipartite networks where both nodes and links are added with time. We use this representation to predict which items might become popular in the near future. Various prediction methods are evaluated on three distinct datasets originating from popular online services (Movielens, Netflix, and Digg). We show that the prediction performance can be further enhanced if the user social network is known and centrality of individual users in this network is used to weight their actions.

Connected through hyperedge HE-1 (induced by source paper "enhancing countries fitness with recommender systems on the international trade network"):
  with members:
    - "network based recommendation algorithms a review"
    - "the essential role of time in network based recommendation"
    - "solving the apparent diversity accuracy dilemma of recommender systems"
    - "predicting the popularity of online content"

Connected through hyperedge HE-2 (induced by source paper "emerging trends in evolving networks recent behaviour dominant and non dominant model"):
  with members:
    - "do cascades recur"
    - "temporal effects in trend prediction identifying the most popular nodes in the future"
    - "quantifying long term scientific impact"

Connected through hyperedge HE-3 (induced by source paper "network based recommendation algorithms a review"):
  with members:
    - "a vertex similarity index for better personalized recommendation"
    - "modeling mutual feedback between users and recommender systems"
    - "information filtering by similarity preferential diffusion processes"
    - "information filtering in sparse online systems recommendation via semi local diffusion"
    - "network based information filtering algorithms ranking and recommendation"
    - "tag aware recommender systems a state of the art survey"
    - "solving the apparent diversity accuracy dilemma of recommender systems"
    - "improved collaborative filtering algorithm via information transformation"

Connected through hyperedge HE-4 (induced by source paper "the essential role of time in network based recommendation"):
  with members:
    - "do recommender systems benefit users a modeling approach"
    - "information filtering by similarity preferential diffusion processes"
    - "social contagion an empirical study of information spread on digg and twitter follower graphs"

Connected through hyperedge HE-5 (induced by source paper "predicting item popularity analysing local clustering behaviour of users"):
  with members:
    - "identifying influential nodes in bipartite networks using the clustering coefficient"

Connected through hyperedge HE-6 (induced by source paper "temporal effects in trend prediction identifying the most popular nodes in the future"):
  with members:
    - "quantifying long term scientific impact"
    - "the pulse of news in social media forecasting popularity"
    - "trends in social media persistence and decay"
    - "using a model of social dynamics to predict popularity of news"
    - "harvesting collective intelligence temporal behavior in yahoo answers"
    - "predicting the popularity of online content"

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)