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: "coarse network coding a simple relay strategy to resolve interference"
  Abstract: Reminiscent of the parity function in network coding for the butterfly network, it is shown that forwarding an even/odd indicator bit for a scalar quantization of a relay observation recovers 1 bit of information at the two destinations in a noiseless interference channel where interference is treated as noise. Based on this observation, a coding strategy is proposed to improve the rate of both users at the same time using a relay node in an interference channel. In this strategy, the relay observes a linear combination of the two source signals, and broadcasts a common message to the two destinations over a shared out-of-band link of rate R0 bits per channel use. The relay message consists of the bin index of a structured binning scheme obtained from a 2^R0-way partition of the squared lattice in the complex plane. We show that such scalar quantization-binning relay strategy asymptotically achieves the cut-set bound in an interference channel with a common out-of-band relay link of limited rate, improving the sum rate by two bits for every bit relayed, asymptotically at high signal to noise ratios (SNR) and when interference is treated as noise. We then use low-density parity-check (LDPC) codes along with bit-interleaved coded-modulation (BICM) as a practical coding scheme for the proposed strategy. We consider matched and mismatched scenarios, depending on whether the input alphabet of the interference signal is known or unknown to the decoder, respectively. For the matched scenario, we show the proposed strategy results in significant gains in SNR. For the mismatched scenario, we show that the proposed strategy results in rate improvements that, without the relay, cannot be achieved by merely increasing transmit powers. Finally, we use generalized mutual information analysis to characterize the theoretical performance of the mismatched scenario and validate our simulation results.

Connected through hyperedge HE-1 (induced by source paper "relay strategies based on cross determinism for the broadcast relay channel"):
  with members:
    - "wireless network information flow a deterministic approach"
    - "capacity of a class of broadcast relay channels"
    - "capacity of a class of deterministic relay channels"
    - "capacity of a class of modulo sum relay channels"
    - "rate regions for relay broadcast channels"

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)