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: "symbol and bit mapping optimization for physical layer network coding with pulse amplitude modulation"
  Abstract: In this paper, we consider a two-way relay network in which two users exchange messages through a single relay using a physical-layer network coding (PNC) based protocol. The protocol comprises two phases of communication. In the multiple access (MA) phase, two users transmit their modulated signals concurrently to the relay, and in the broadcast (BC) phase, the relay broadcasts a network-coded (denoised) signal to both users. Nonbinary and binary network codes are considered for uniform and nonuniform pulse amplitude modulation (PAM) adopted in the MA phase, respectively. We examine the effect of different choices of symbol mapping (i.e., mapping from the denoised signal to the modulation symbols at the relay) and bit mapping (i.e., mapping from the modulation symbols to the source bits at the user) on the system error-rate performance. A general optimization framework is proposed to determine the optimal symbol/bit mappings with joint consideration of noisy transmissions in both communication phases. Complexity-reduction techniques are developed for solving the optimization problems. It is shown that the optimal symbol/bit mappings depend on the signal-to-noise ratio (SNR) of the channel and the modulation scheme. A general strategy for choosing good symbol/bit mappings is also presented based on a high-SNR analysis, which suggests using a symbol mapping that aligns the error patterns in both communication phases and Gray and binary bit mappings for uniform and nonuniform PAM, respectively.

Connected through hyperedge HE-1 (induced by source paper "on the subtleties of q pam linear physical layer network coding"):
  with members:
    - "multilevel coding schemes for compute and forward with flexible decoding"
    - "an algebraic approach to physical layer network coding"
    - "asynchronous physical layer network coding"
    - "multiple input multiple output two way relaying a space division approach"
    - "reliable physical layer network coding"
    - "physical layer network coding tutorial survey and beyond"
    - "implementation of physical layer network coding"
    - "channel coding and decoding in a relay system operated with physical layer network coding"

Connected through hyperedge HE-2 (induced by source paper "complex linear physical layer network coding"):
  with members:
    - "on the subtleties of q pam linear physical layer network coding"
    - "lattices over eisenstein integers for compute and forward"
    - "multilevel coding schemes for compute and forward with flexible decoding"
    - "an algebraic approach to physical layer network coding"
    - "reliable physical layer network coding"
    - "physical layer network coding tutorial survey and beyond"
    - "channel coding and decoding in a relay system operated with physical layer network coding"
    - "compute and forward harnessing interference through structured codes"

Connected through hyperedge HE-3 (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"
    - "error performance analysis of df and af multi way relay networks with bpsk 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)