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: "social content matching in mapreduce"
  Abstract: Matching problems are ubiquitous. They occur in economic markets, labor markets, internet advertising, and elsewhere. In this paper we focus on an application of matching for social media. Our goal is to distribute content from information suppliers to information consumers. We seek to maximize the overall relevance of the matched content from suppliers to consumers while regulating the overall activity, e.g., ensuring that no consumer is overwhelmed with data and that all suppliers have chances to deliver their content. #R##N#We propose two matching algorithms, GreedyMR and StackMR, geared for the MapReduce paradigm. Both algorithms have provable approximation guarantees, and in practice they produce high-quality solutions. While both algorithms scale extremely well, we can show that StackMR requires only a poly-logarithmic number of MapReduce steps, making it an attractive option for applications with very large datasets. We experimentally show the trade-offs between quality and efficiency of our solutions on two large datasets coming from real-world social-media web sites.

Connected through hyperedge HE-1 (induced by source paper "scalable facility location for massive graphs on pregel like systems"):
  with members:
    - "all distances sketches revisited hip estimators for massive graphs analysis"
    - "greedy sequential maximal independent set and matching are parallel on average"
    - "densest subgraph in streaming and mapreduce"
    - "hyperanf approximating the neighbourhood function of very large graphs on a budget"
    - "parallel approximation algorithms for facility location problems"
    - "greedy facility location algorithms analyzed using dual fitting with factor revealing lp"

Connected through hyperedge HE-2 (induced by source paper "show me the money dynamic recommendations for revenue maximization"):
  with members:
    - "maximizing profit using recommender systems"

Connected through hyperedge HE-3 (induced by source paper "the family of mapreduce and large scale data processing systems"):
  with members:
    - "distributed graphlab a framework for machine learning in the cloud"
    - "restore reusing results of mapreduce jobs"
    - "v smart join a scalable mapreduce framework for all pair similarity joins of multisets and vectors"
    - "stubby a transformation based optimizer for mapreduce workflows"
    - "automatic optimization for mapreduce programs"
    - "fast clustering using mapreduce"
    - "load balancing for mapreduce based entity resolution"
    - "column oriented storage techniques for mapreduce"

Connected through hyperedge HE-4 (induced by source paper "densest subgraph in streaming and mapreduce"):
  with members:
    - "multi skill collaborative teams based on densest subgraphs"
    - "community structure in large networks natural cluster sizes and the absence of large well defined clusters"

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)