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 "beamforming tradeoffs for initial ue discovery in millimeter wave mimo systems"):
  Abstract: Millimeter-wave (mmW) multi-input multi-output (MIMO) systems have gained increasing traction toward the goal of meeting the high data-rate requirements in next-generation wireless systems. The focus of this work is on low-complexity beamforming approaches for initial user equipment (UE) discovery in such systems. Toward this goal, we first note the structure of the optimal beamformer with per-antenna gain and phase control and establish the structure of good beamformers with per-antenna phase-only control. Learning these right singular vector (RSV)-type beamforming structures in mmW systems is fraught with considerable complexities such as the need for a non-broadcast system design, the sensitivity of the beamformer approximants to small path length changes, inefficiencies due to power amplifier backoff, etc. To overcome these issues, we establish a physical interpretation between the RSV-type beamformer structures and the angles of departure/arrival (AoD/AoA) of the dominant path(s) capturing the scattering environment. This physical interpretation provides a theoretical underpinning to the emerging interest on directional beamforming approaches that are less sensitive to small path length changes. While classical approaches for direction learning such as MUltiple SIgnal Classification (MUSIC) have been well-understood, they suffer from many practical difficulties in a mmW context such as a non-broadcast system design and high computational complexity. A simpler broadcast-based solution for mmW systems is the adaptation of limited feedback-type directional codebooks for beamforming at the two ends. We establish fundamental limits for the best beam broadening codebooks and propose a construction motivated by a virtual subarray architecture that is within a couple of dB of the best tradeoff curve at all useful beam broadening factors. We finally provide the received   ${\text{SNR}}$   loss-UE discovery latency tradeoff with the proposed beam broadening constructions. Our results show that users with a reasonable link margin can be quickly discovered by the proposed design with a smooth roll-off in performance as the link margin deteriorates. While these designs are poorer in performance than the RSV learning approaches or MUSIC for cell-edge users, their low-complexity that leads to a broadcast system design makes them a useful candidate for practical mmW systems.

Includes members:
  Member "directional beamforming for millimeter wave mimo systems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "hand and body blockage measurements with form factor user equipment at 28 ghz")
  Member "millimeter wave cellular networks a mac layer perspective":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multi user provisioning in millimeter wave urban cellular networks")
  Member "directional cell discovery in millimeter wave cellular networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a tutorial on beam management for 3gpp nr at mmwave frequencies")
  Member "channel estimation and hybrid precoding for millimeter wave cellular systems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep learning for massive mimo channel state acquisition and feedback")
  Member "millimeter wave cellular wireless networks potentials and challenges":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "wirelessly powered sensor networks power allocation for channel estimation and energy beamforming")
  Member "spatially sparse precoding in millimeter wave mimo systems":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "energy efficient hybrid precoding design for integrated multicast unicast millimeter wave communications with swipt")
  Member "millimeter wave channel modeling and cellular capacity evaluation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "coverage analysis of uavs in millimeter wave networks a stochastic geometry approach")
  Member "statistical beamforming on the grassmann manifold for the two user broadcast channel":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "statistical beamforming for fdd downlink massive mimo via spatial information extraction and beam selection")

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)