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: "how many mice make an elephant modelling flow length and size distribution of internet traffic"
  Abstract: The efficiency of numerous proposed flow-oriented networking mechanisms strongly depends on traffic characteristics and should thus be assessed using accurate models. For example, in the case of algorithms, which are based on the distinction between elephant and mice flows, it is extremely important to ensure realistic flows' length (in packets) and size (in bytes) distributions. Credible models or data are not available in literature. Numerous works contain only plots roughly presenting empirical probability density functions and cumulative distribution functions of selected flow parameters, however, none of these papers provide distribution mixture models or reusable data. This paper presents a complete tutorial on constructing network flow models from traffic traces. The proposed methodology is universal and can be applied to traffic traces gathered in any network. Apart from the methodology, we also provide an open source software framework which make it possible to analyse flow traces and fit general mixture models to them. To the best of our knowledge, this is the first work that provides such a universal methodology together with fully a usable software framework. Additionally, we utilise the proposed methodology and software to extract accurate models of flow length and size from real traffic traces. Traces were collected at the Internet-facing interface of the university campus network and comprise four billion flow (275 TB of transmitted data). Thus, this work also provides a realistic model of traffic, which can be used to assess a variety of flow-oriented solutions under the assumption of realistic conditions.

Connected through hyperedge HE-1 (induced by source paper "taurus an intelligent data plane"):
  with members:
    - "scaling distributed machine learning with in network aggregation"
    - "meta learning neural bloom filters"
    - "fpga based accelerators of deep learning networks for learning and classification a review"
    - "deep neural network approximation for custom hardware where we ve been where we re going"
    - "experience driven networking a deep reinforcement learning based approach"
    - "in network neural networks"
    - "structured control nets for deep reinforcement learning"
    - "training deep neural networks with 8 bit floating point numbers"

Connected through hyperedge HE-2 (induced by source paper "testing implementation of famtar adaptive multipath routing"):
  with members:
    - "an optimized bbr for multipath real time video streaming"
    - "network congestion control with markovian multipath routing"

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)