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: "error performance analysis of df and af multi way relay networks with bpsk modulation"
  Abstract: In this paper, we analyze the error performance of decode and forward (DF) and amplify and forward (AF) multi-way relay networks (MWRN). We consider a MWRN with pair-wise data exchange protocol using binary phase shift keying (BPSK) modulation in both additive white Gaussian noise (AWGN) and Rayleigh fading channels. We quantify the possible error events in an $L$-user DF or AF MWRN and derive accurate asymptotic bounds on the probability for the general case that a user incorrectly decodes the messages of exactly $k$ ($k\in[1,L-1]$) users. We show that at high signal-to-noise ratio (SNR), the higher order error events ($k\geq 3$) are less probable in AF MWRN, but all error events are equally probable in a DF MWRN. We derive the average BER of a user in a DF or AF MWRN in both AWGN and Rayleigh fading channels under high SNR conditions. Simulation results validate the correctness of the derived expressions. Our results show that at medium to high SNR, DF MWRN provides better error performance than AF MWRN in AWGN channels even with a large number of users (for example, L=100). Whereas, AF MWRN outperforms DF MWRN in Rayleigh fading channels even for much smaller number of users (for example, $L > 10$).

Connected through hyperedge HE-1 (induced by source paper "a novel user pairing scheme for functional decode and forward multi way relay network"):
  with members:
    - "optimal coding functions for pairwise message sharing on finite field multi way relay channels"
    - "on the achievable rates of pairwise multiway relay channels"
    - "symbol and bit mapping optimization for physical layer network coding with pulse amplitude modulation"
    - "on the equal rate capacity of the awgn multiway relay channel"
    - "asynchronous physical layer network coding"
    - "nested lattice codes for gaussian relay networks with interference"
    - "physical layer network coding tutorial survey and beyond"
    - "lattice codes for the gaussian relay channel decode and forward and compress and forward"

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)