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 "vlsi designs for joint channel estimation and data detection in large simo wireless systems"):
  Abstract: Channel estimation errors have a critical impact on the reliability of wireless communication systems. While virtually all existing wireless receivers separate channel estimation from data detection, it is well known that joint channel estimation and data detection (JED) significantly outperforms conventional methods at the cost of high computational complexity. In this paper, we propose a novel JED algorithm and corresponding VLSI designs for large single-input multiple-output (SIMO) wireless systems that use constant-modulus constellations. The proposed algorithm is referred to as PRojection Onto conveX hull (PrOX) and relies on biconvex relaxation (BCR), which enables us to efficiently compute an approximate solution of the maximum-likelihood JED problem. Since BCR solves a biconvex problem via alternating optimization, we provide a theoretical convergence analysis for PrOX. We design a scalable, high-throughput VLSI architecture that uses a linear array of processing elements to minimize hardware complexity. We develop corresponding field-programmable gate array (FPGA) and application-specific integrated circuit (ASIC) designs, and we demonstrate that PrOX significantly outperforms the only other existing JED design in terms of throughput, hardware-efficiency, and energy-efficiency.

Includes members:
  Member "optimally tuned nonparametric linear equalization for massive mu mimo systems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "decentralized equalization with feedforward architectures for massive mu mimo")
  Member "efficient optimal joint channel estimation and data detection for massive mimo systems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "efficient dsp and circuit architectures for massive mimo state of the art and future directions")
  Member "bayes optimal joint channel and data estimation for massive mimo with low precision adcs":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "faster activity and data detection in massive random access a multi armed bandit approach")
  Member "joint channel estimation data detection in mimo fbmc oqam systems a tensor based approach":
    also connected through other hyperedges:
      (none)
  Member "data detection in large multi antenna wireless systems via approximate semidefinite relaxation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "1 bit massive mu mimo precoding in vlsi")
  Member "high throughput data detection for massive mu mimo ofdm using coordinate descent":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "efficient dsp and circuit architectures for massive mimo state of the art and future directions")
  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-6 (induced by source paper "data detection in large multi antenna wireless systems via approximate semidefinite relaxation")
  Member "fifty years of mimo detection the road to large scale mimos":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "model driven deep learning for joint mimo channel estimation and signal detection")

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)