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: "network based information filtering algorithms ranking and recommendation"
  Abstract: This chapter gives an overview of applications of random walks to information filtering, focusing on the tasks of ranking and recommendation in particular. Despite the amount of work done in these two directions, multiple important research challenges still remain open. Due to the massive amounts of available data, scalability of algorithms is of critical importance. Even when full computation is possible, one can think of potential approaches to update the output gradually when new data arrives. To achieve that, one can use or learn from perturbation theory which is a well-known tool in physics. It has been seen that results based on random walks often correlate strongly with mere popularity (represented by degree) of nodes in the network. Such bias toward popularity may be beneficial for an algorithm’s accuracy but it may also narrow one’s view of the given system and perhaps create a self reinforcing loop further boosting popularity of already popular nodes. Thus it is needed that information filtering algorithms converge less to the center of the given network. Random walks biased by node centrality or time information about nodes and links could provide a solution to this problem. As a beneficial side effect, this line of research could yield algorithms pointing us to fresh and promising content instead of highlighting old victors over and over again.

Connected through hyperedge HE-1 (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"
    - "trend prediction in temporal bipartite networks the case of movielens netflix and digg"
    - "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"

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)