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: "an empirical view on content provider fairness"
  Abstract: Congestion control is an indispensable component of transport protocols to prevent congestion collapse. As such, it distributes the available bandwidth among all competing flows, ideally in a fair manner. However, there exists a constantly evolving set of congestion control algorithms, each addressing different performance needs and providing the potential for custom parametrizations. In particular, content providers such as CDNs are known to tune TCP stacks for performance gains. In this paper, we thus empirically investigate if current Internet traffic generated by content providers still adheres to the conventional understanding of fairness. Our study compares fairness properties of testbed hosts to actual traffic of six major content providers subject to different bandwidths, RTTs, queue sizes, and queueing disciplines in a home-user setting. We find that some employed congestion control algorithms lead to significantly asymmetric bandwidth shares, however, AQMs such as FQ_CoDel are able to alleviate such unfairness.

Connected through hyperedge HE-1 (induced by source paper "configtron tackling network diversity with heterogeneous configurations"):
  with members:
    - "beyond socket options making the linux tcp stack truly extensible"
    - "iroko a framework to prototype reinforcement learning for data center traffic control"
    - "internet congestion control via deep reinforcement learning"
    - "bestconfig tapping the performance potential of systems via automatic configuration tuning"
    - "raptor routing attacks on privacy in tor"
    - "pcc re architecting congestion control for consistent high performance"
    - "a tutorial on bayesian optimization of expensive cost functions with application to active user modeling and hierarchical reinforcement learning"

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.NI(Networking and Internet Architecture)