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 a mixed strategy for multiple relay networks"
  Abstract: In their landmark paper Cover and El Gamal proposed different coding strategies for the relay channel with a single relay supporting a communication pair. These strategies are the decode-and-forward and compress-and-forward approach, as well as a general lower bound on the capacity of a relay network which relies on the mixed application of the previous two strategies. So far, only parts of their work - the decode-and-forward and the compress-and-forward strategy - have been applied to networks with multiple relays. #R##N#This paper derives a mixed strategy for multiple relay networks using a combined approach of partial decode-and-forward with N +1 levels and the ideas of successive refinement with different side information at the receivers. After describing the protocol structure, we present the achievable rates for the discrete memoryless relay channel as well as Gaussian multiple relay networks. Using these results we compare the mixed strategy with some special cases, e. g., multilevel decode-and-forward, distributed compress-and-forward and a mixed approach where one relay node operates in decode-and-forward and the other in compress-and-forward mode.

Connected through hyperedge HE-1 (induced by source paper "a unified relay framework with both d f and c f relay nodes"):
  with members:
    - "compress and forward scheme for relay networks backword decoding and connection to bisubmodular flows"
    - "selective coding strategy for unicast composite networks"
    - "wireless network information flow a deterministic approach"
    - "short message quantize forward network coding"
    - "on the optimal compressions in the compress and forward relay schemes"
    - "noisy network coding"
    - "lecture notes on network information theory"
    - "an improvement of cover el gamal s compress and forward relay scheme"

Connected through hyperedge HE-2 (induced by source paper "slepian wolf coding over cooperative relay networks"):
  with members:
    - "wireless network information flow a deterministic approach"
    - "deterministic relay networks with state information"
    - "wyner ziv coding over broadcast channels digital schemes"
    - "approximate capacity of gaussian relay networks"
    - "protocols for half duplex multiple relay networks"

Connected through hyperedge HE-3 (induced by source paper "protocols and performance limits for half duplex relay networks"):
  with members:
    - "superposition coded concurrent decode and forward relaying"

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)