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 "optimality of large mimo detection via approximate message passing"):
  Abstract: Optimal data detection in multiple-input multiple-output (MIMO) communication systems with a large number of antennas at both ends of the wireless link entails prohibitive computational complexity. In order to reduce the computational complexity, a variety of sub-optimal detection algorithms have been proposed in the literature. In this paper, we analyze the optimality of a novel data-detection method for large MIMO systems that relies on approximate message passing (AMP). We show that our algorithm, referred to as individually-optimal (IO) large-MIMO AMP (short IO-LAMA), is able to perform IO data detection given certain conditions on the MIMO system and the constellation set (e.g., QAM or PSK) are met.

Includes members:
  Member "large scale mimo detection for 3gpp lte algorithms and fpga implementations":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "decentralized equalization with feedforward architectures for massive mu mimo")
  Member "massive mimo for next generation wireless systems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "joint power control and lsfd for wireless powered cell free massive mimo")
  Member "a factor graph approach to joint ofdm channel estimation and decoding in impulsive noise environments":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "capacity optimality of amp in coded systems")
  Member "channel hardening exploiting message passing chemp receiver in large scale mimo systems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on the pilot contamination attack in multi cell multiuser massive mimo networks")
  Member "sumis near optimal soft in soft out mimo detection with low and fixed complexity":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "fifty years of mimo detection the road to large scale mimos")
  Member "asymptotic analysis of complex lasso via complex approximate message passing camp":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "optimal errors and phase transitions in high dimensional generalized linear models")
  Member "a message passing receiver for bicm ofdm over unknown clustered sparse channels":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "capacity optimality of amp in coded systems")
  Member "the dynamics of message passing on dense graphs with applications to compressed sensing":
    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)