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: "analysis of csat performance in wi fi and lte u coexistence"
  Abstract: In this paper, we study energy-based Carrier Sense Adaptive Transmission (CSAT) for use with LTE-U and investigate the performance in Wi-Fi/LTE-U coexistence using theoretical analysis and experimental verification using NI USRPs. According to the LTE-U forum specification, if an LTE-U base station (BS) finds a vacant channel, it can transmit for up to 20 ms and turn OFF its transmission for only 1 ms, resulting in a maximum duty cycle of 95%. In a dense deployment of LTE-U and Wi-Fi, it is very likely that a Wi-Fi access point (AP) will wish to use the same channel. It will start transmission by trying to transmit association packets (using carrier sense multiple access with collision avoidance (CSMA/CA)) through the 1 ms LTE-U OFF duration. Since this duration is very small, it leads to increased association packet drops and thus delays the Wi-Fi association process. Once LTE-U, using CSAT, detects Wi-Fi, it should scale back the duty cycle to 50%. We demonstrate in this paper, using an experimental platform as well as theoretical analysis, that if LTE-U is using a 95% duty cycle, energy based CSAT will take a much longer time to scale back the duty cycle due to the beacon drops and delays in the reception. Hence, in order to maintain association fairness with Wi-Fi, we propose that a LTE-U BS should not transmit at maximum duty cycles (95%), even if the channel is sensed to be vacant.

Connected through hyperedge HE-1 (induced by source paper "machine learning based detection of multiple wi fi bsss for lte u csat"):
  with members:
    - "deeplens towards a visual data management system"
    - "deep learning in mobile and wireless networking a survey"
    - "in datacenter performance analysis of a tensor processing unit"
    - "time series classification from scratch with deep neural networks a strong baseline"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "fast algorithms for convolutional neural networks"
    - "very deep convolutional networks for large scale image recognition"
    - "fast convolutional nets with fbfft a gpu performance evaluation"

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)