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 "a proximal jacobian admm approach for fast massive mimo signal detection in low latency communications"):
  Abstract: One of the 5G promises is to provide Ultra Reliable Low Latency Communications (URLLC) which targets an end to end communication latency that is less than 1ms . The very low latency requirement of URLLC entails a lot of work in all networking layers. In this paper, we focus on the physical layer, and in particular, we propose a novel formulation of the massive MIMO uplink detection problem. We introduce an objective function that is a sum of strictly convex and separable functions based on decomposing the received vector into multiple vectors. Each vector represents the contribution of one of the transmitted symbols in the received vector. Proximal Jacobian Alternating Direction Method of Multipliers (PJADMM) is used to solve the new formulated problem in an iterative manner where at every iteration all variables are updated in parallel and in a closed form expression. The proposed algorithm provides a lower complexity and much faster processing time compared to the conventional MMSE detection technique and other iterative-based techniques, especially when the number of single antenna users is close to the number of base station (BS) antennas. This improvement is obtained without any matrix inversion. Simulation results demonstrate the efficacy of the proposed algorithm in reducing detection processing time in the multi-user uplink massive MIMO setting.

Includes members:
  Member "resource allocation and harq optimization for urllc traffic in 5g wireless networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "robust urllc packet scheduling of ofdm systems")
  Member "ultra reliable and low latency wireless communication tail risk and scale":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "optimized age of information tail for ultra reliable low latency communications in vehicular networks")
  Member "a low complexity detection algorithm based on alternating minimization":
    also connected through other hyperedges:
      (none)
  Member "two stage lasso admm signal detection algorithm for large scale mimo":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning to search for mimo detection")
  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 "efficient dsp and circuit architectures for massive mimo state of the art and future directions")
  Member "living on the edge the role of proactive caching in 5g wireless networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "aoi delay tradeoff in mobile edge caching with freshness aware content refreshing")
  Member "five disruptive technology directions for 5g":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deep learning enabled optimization of downlink beamforming under per antenna power constraints algorithms and experimental demonstration")

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)