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: "novel performance analysis of network coded communications in single relay networks"
  Abstract: In this paper, we analyze the performance of a single-relay network in which the reliability is provided by means of Random Linear Network Coding (RLNC). We consider a scenario when both source and relay nodes can encode packets. Unlike the traditional approach to relay networks, we introduce a passive relay mode, in which the relay node simply retransmits collected packets in case it cannot decode them. In contrast with the previous studies, we derive a novel theoretical framework for the performance characterization of the considered relay network. We extend our analysis to a more general scenario, in which coding coefficients are generated from non-binary fields. The theoretical results are verified using simulation, for both binary and non-binary fields. It is also shown that the passive relay mode significantly improves the performance compared with the active-only case, offering an up to two-fold gain in terms of the decoding probability. The proposed framework can be used as a building block for the analysis of more complex network topologies.

Connected through hyperedge HE-1 (induced by source paper "reliability of relay networks under random linear network coding"):
  with members:
    - "reliability of multicast under random linear network coding"
    - "decoding delay performance of random linear network coding for broadcast"
    - "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-2 (induced by source paper "reliability of multicast under random linear network coding"):
  with members:
    - "reliability of broadcast communications under sparse random linear network coding"
    - "decoding delay performance of random linear network coding for broadcast"
    - "resource allocation frameworks for network coded layered multimedia multicast services"
    - "binary systematic network coding for progressive packet decoding"
    - "optimized network coded scalable video multicasting over embms networks"
    - "performance analysis of random linear network coding in two source single relay networks"
    - "rethinking the intercept probability of random linear network coding"
    - "network coding meets multimedia a review"

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)