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 "optimal link scheduling in millimeter wave multi hop networks with mu mimo radios"):
  Abstract: This paper studies the maximum throughput achievable with optimal scheduling in multi-hop mmWave picocellular networks with Multi-user Multiple-Input Multiple-Output (MU-MIMO) radios. MU-MIMO enables simultaneous transmission to multiple receivers (Space Division Multiplexing) and simultaneous reception from multiple transmitters (Space Division Multiple Access). The main contribution is the extension to MU-MIMO of the Network Utility Maximization (NUM) scheduling framework for multi-hop networks. We generalize to MU-MIMO the classic proof that Maximum Back Pressure (MBP) scheduling is NUM optimal. MBP requires the solution of an optimization that becomes harder with MU-MIMO radios. In prior models with one-to-one transmission and reception, each valid schedule was a matching over a graph. However, with MU-MIMO each valid schedule is, instead, a Directed Bipartite SubGraph (DBSG). In the general case this prevents finding efficient algorithms to solve the scheduler. We make MU-MIMO MBP scheduling tractable by assuming fixed power allocation, so the optimal scheduler is the Maximum Weighted DBSG. The MWDBSG problem can be solved using standard Mixed Integer Linear Programing. We simulate multi-hop mmWave picocellular networks and show that a MU-MIMO MBP scheduler enables a 160% increase in network throughput versus the classic one-to-one MBP scheduler, while fair rate allocation mechanisms are used in both cases.

Includes members:
  Member "integrated access and backhaul in 5g mmwave networks potentials and challenges":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "millimeter wave integrated access and backhaul in 5g performance analysis and design insights")
  Member "distributed path selection strategies for integrated access and backhaul at mmwaves":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "millimeter wave integrated access and backhaul in 5g performance analysis and design insights")
  Member "hierarchical codebook design for beamforming training in millimeter wave communication":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "edge cache assisted secure low latency millimeter wave transmission")
  Member "understanding noise and interference regimes in 5g millimeter wave cellular networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "joint path selection and rate allocation framework for 5g self backhauled mm wave networks")
  Member "codebook design for millimeter wave channel estimation with hybrid precoding structure":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "channel estimation and hybrid precoding for distributed phased arrays based mimo wireless communications")
  Member "hybrid architectures with few bit adc receivers achievable rates and energy rate tradeoffs":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "capacity bounds for communication systems with quantization and spectral constraints")
  Member "towards an appropriate receiver beamforming scheme for millimeter wave communication a power consumption based comparison":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "standalone and non standalone beam management for 3gpp nr at mmwaves")
  Member "massive mimo ten myths and one critical question":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "deep learning for massive mimo channel state acquisition and feedback")

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)