human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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).

Includes members:
  Member "capacity achieving mimo noma iterative lmmse detection":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a new insight into gamp and amp")
  Member "a unified framework of state evolution for message passing algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "convolutional approximate message passing")
  Member "optimal data detection in large mimo":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "cnn based signal detection for banded linear systems")
  Member "near gaussian multiple access channel capacity detection and decoding":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "capacity achieving mimo noma iterative lmmse detection")
  Member "approximate message passing decoder and capacity achieving sparse superposition codes":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "capacity achieving spatially coupled sparse superposition codes with amp decoding")
  Member "capacity achieving sparse superposition codes via approximate message passing decoding":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "capacity achieving spatially coupled sparse superposition codes with amp decoding")
  Member "mutual information and optimality of approximate message passing in random linear estimation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "unsourced multiuser sparse regression codes achieve the symmetric mac capacity")
  Member "compressive phase retrieval via generalized approximate message passing":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "capacity achieving spatially coupled sparse superposition codes with amp decoding")

Question: Please tell me which class the source paper 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)