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 "data detection in large multi antenna wireless systems via approximate semidefinite relaxation"):
  Abstract: Practical data detectors for future wireless systems with hundreds of antennas at the base station must achieve high throughput and low error rate at low complexity. Since the complexity of maximum-likelihood (ML) data detection is prohibitive for such large wireless systems, approximate methods are necessary. In this paper, we propose a novel data detection algorithm referred to as Triangular Approximate SEmidefinite Relaxation (TASER), which is suitable for two application scenarios: (i) coherent data detection in large multi-user multiple-input multiple-output (MU-MIMO) wireless systems and (ii) joint channel estimation and data detection in large single-input multiple-output (SIMO) wireless systems. For both scenarios, we show that TASER achieves near-ML error-rate performance at low complexity by relaxing the associated ML-detection problems into a semidefinite program, which we solve approximately using a preconditioned forward-backward splitting procedure. Since the resulting problem is non-convex, we provide convergence guarantees for our algorithm. To demonstrate the efficacy of TASER in practice, we design a systolic architecture that enables our algorithm to achieve high throughput at low hardware complexity, and we develop reference field-programmable gate array (FPGA) and application-specific integrated circuit (ASIC) designs for various antenna configurations.

Includes members:
  Member "optimality of large mimo detection via approximate message passing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "capacity achieving spatially coupled sparse superposition codes with amp decoding")
  Member "optimal non coherent data detection for massive simo wireless systems with general constellations a polynomial complexity solution":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "vlsi designs for joint channel estimation and data detection in large simo wireless systems")
  Member "a low complexity gaussian message passing iterative detector for massive mu mimo systems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "capacity achieving iterative lmmse detection for mimo noma systems")
  Member "large scale mimo detection for 3gpp lte algorithms and fpga implementations":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "decentralized equalization with feedforward architectures for massive mu mimo")
  Member "conjugate gradient based soft output detection and precoding in massive mimo systems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a proximal jacobian admm approach for fast massive mimo signal detection in low latency communications")
  Member "a field guide to forward backward splitting with a fasta implementation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "towards compact convnets via structure sparsity regularized filter pruning")
  Member "massive mimo for next generation wireless systems":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "joint power control and lsfd for wireless powered cell free massive mimo")
  Member "what will 5g be":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "5g network slicing using sdn and nfv a survey of taxonomy architectures and future challenges")

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)