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: "mixture models of endhost network traffic"
  Abstract: In this work we focus on modeling a little studied type of traffic, namely the network traffic generated from endhosts. We introduce a parsimonious parametric model of the marginal distribution for connection arrivals. We employ mixture models based on a convex combination of component distributions with both heavy and light-tails. These models can be fitted with high accuracy using maximum likelihood techniques. Our methodology assumes that the underlying user data can be fitted to one of many modeling options, and we apply Bayesian model selection criteria as a rigorous way to choose the preferred combination of components. Our experiments show that a simple Pareto-exponential mixture model is preferred for a wide range of users, over both simpler and more complex alternatives. This model has the desirable property of modeling the entire distribution, effectively segmenting the traffic into the heavy-tailed as well as the non-heavy-tailed components. We illustrate that this technique has the flexibility to capture the wide diversity of user behaviors.

Connected through hyperedge HE-1 (induced by source paper "unsupervised machine learning for networking techniques applications and research challenges"):
  with members:
    - "federated learning strategies for improving communication efficiency"
    - "deep learning models for wireless signal classification with distributed low cost spectrum sensors"
    - "a survey of machine and deep learning methods for internet of things iot security"
    - "an introduction to deep learning for the physical layer"
    - "shallow and deep networks intrusion detection system a taxonomy and survey"
    - "iot data analytics using deep learning"
    - "from 4g to 5g self organized network management meets machine learning"
    - "why and how networks should run themselves"

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)