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: "capacity optimality of amp in coded systems"
  Abstract: This paper studies a random linear system with arbitrary input distributions, whose constrained capacity is recently derived in literature. However, how to find a practical encoder and receiver to achieve this capacity still remains an open problem. In this paper, we establish an area property for AMP in coded systems. With the correctness assumption of state evolution, the achievable rate of AMP for the coded random linear system is analyzed following the code-rate-minimum mean-square error (MMSE) lemma. We prove that the low-complexity AMP achieves the constrained capacity based on matched forward error control (FEC) coding. As a byproduct, we provide an alternative concise derivation for the constrained capacity by taking advantage of the properties of AMP. As examples, Gaussian, quadrature phase shift keying (QPSK), 8PSK, and 16 quadrature amplitude modulation (16-QAM) inputs are studied as special instances. We show that the designed AMP receiver has a significant improvement in achievable rate comparing with the conventional Turbo method and the state-of-art separate detection and decoding scheme. Irregular low-density parity-check (LDPC) codes are designed for AMP to obtain capacity-approaching performances (within 1 dB away from the capacity limit).

Connected through hyperedge HE-1 (induced by source paper "a new insight into gamp and amp"):
  with members:
    - "capacity achieving mimo noma iterative lmmse detection"
    - "a unified bayesian inference framework for generalized linear models"
    - "bilinear adaptive generalized vector approximate message passing"
    - "gaussian message passing for overloaded massive mimo noma"
    - "massive connectivity with massive mimo part i device activity detection and channel estimation"
    - "beam on graph simultaneous channel estimation for mmwave mimo systems with multiple users"
    - "rigorous dynamics of expectation propagation based signal recovery from unitarily invariant measurements"
    - "hybrid approximate message passing"

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)