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: "reliability of multicast under random linear network coding"
  Abstract: We consider a lossy multicast network in which the reliability is provided by means of Random Linear Network Coding. Our goal is to characterise the performance of such network in terms of the probability that a source message is delivered to all destination nodes. Previous studies considered coding over large finite fields, small numbers of destination nodes or specific, often impractical, channel conditions. In contrast, we focus on a general problem, considering arbitrary field size and number of destination nodes, as well as a realistic channel. We propose a lower bound on the probability of successful delivery, which is more accurate than the approximation commonly used in the literature. In addition, we present a novel performance analysis of the systematic version of RLNC. The accuracy of the proposed performance framework is verified via extensive Monte Carlo simulations, where the impact of the network and code parameters are investigated. Specifically, we show that the mean square error of the bound for a ten-user network can be as low as $9 \cdot 10^{-5}$ for non-systematic RLNC.

Connected through hyperedge HE-1 (induced by source paper "operating its g5 dsrc over unlicensed bands a city scale performance evaluation"):
  with members:
    - "a dataset of full stack its g5 dsrc communications over licensed and unlicensed bands using a large scale urban testbed"
    - "multi radio 5g architecture for connected and autonomous vehicles application and design insights"
    - "a city scale its g5 network for next generation intelligent transportation systems design insights and challenges"

Connected through hyperedge HE-2 (induced by source paper "secure data offloading strategy for connected and autonomous vehicles"):
  with members:
    - "a city scale its g5 network for next generation intelligent transportation systems design insights and challenges"
    - "rethinking the intercept probability of random linear network coding"

Connected through hyperedge HE-3 (induced by source paper "reliability of relay networks under random linear network coding"):
  with members:
    - "decoding delay performance of random linear network coding for broadcast"
    - "novel performance analysis of network coded communications in single relay networks"
    - "improved bounds on the decoding failure probability of network coding over multi source multi relay networks"
    - "performance analysis of random linear network coding in two source single relay networks"
    - "network coding meets multimedia a review"
    - "further results on coding for reliable communication over packet networks"
    - "on coding for reliable communication over packet networks"

Connected through hyperedge HE-4 (induced by source paper "reliability of broadcast communications under sparse random linear network coding"):
  with members:
    - "improved bounds on the decoding failure probability of network coding over multi source multi relay networks"
    - "analysis and optimization of sparse random linear network coding for reliable multicast services"
    - "resource allocation frameworks for network coded layered multimedia multicast services"
    - "network coding meets multimedia a review"

Connected through hyperedge HE-5 (induced by source paper "on intercept probability minimization under sparse random linear network coding"):
  with members:
    - "reliability of broadcast communications under sparse random linear network coding"
    - "analysis and optimization of sparse random linear network coding for reliable multicast services"
    - "rethinking the intercept probability of random linear network coding"

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.IT(Information Theory)