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 "scalable synchronization and reciprocity calibration for distributed multiuser mimo"):
  Abstract: Large-scale distributed Multiuser MIMO (MU-MIMO) is a promising wireless network architecture that combines the advantages of "massive MIMO" and "small cells." It consists of several Access Points (APs) connected to a central server via a wired backhaul network and acting as a large distributed antenna system. We focus on the downlink, which is both more demanding in terms of traffic and more challenging in terms of implementation than the uplink. In order to enable multiuser joint precoding of the downlink signals, channel state information at the transmitter side is required. We consider Time Division Duplex (TDD), where the {\em downlink} channels can be learned from the user uplink pilot signals, thanks to channel reciprocity. Furthermore, coherent multiuser joint precoding is possible only if the APs maintain a sufficiently accurate relative timing and phase synchronization. AP synchronization and TDD reciprocity calibration are two key problems to be solved in order to enable distributed MU-MIMO downlink. In this paper, we propose novel over-the-air synchronization and calibration protocols that scale well with the network size. The proposed schemes can be applied to networks formed by a large number of APs, each of which is driven by an inexpensive 802.11-grade clock and has a standard RF front-end, not explicitly designed to be reciprocal. Our protocols can incorporate, as a building block, any suitable timing and frequency estimator. Here we revisit the problem of joint ML timing and frequency estimation and use the corresponding Cramer-Rao bound to evaluate the performance of the synchronization protocol. Overall, the proposed synchronization and calibration schemes are shown to achieve sufficient accuracy for satisfactory distributed MU-MIMO performance.

Includes members:
  Member "network mimo with linear zero forcing beamforming large system analysis impact of channel estimation and reduced complexity scheduling":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "joint user selection power allocation and precoding design with imperfect csit for multi cell mu mimo downlink systems")
  Member "achieving massive mimo spectral efficiency with a not so large number of antennas":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "performance of multi cell massive mimo systems with interference decoding")
  Member "airsync enabling distributed multiuser mimo with full spatial multiplexing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "distributed base station a concept system for long range broadband wireless access")
  Member "multiuser miso transmitter optimization for intercell interference mitigation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "the capacity and optimal signaling for gaussian mimo channels under interference constraints full version")
  Member "femtocell networks a survey":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "robust resource allocation for pd noma based miso heterogeneous networks with comp technology")
  Member "multiuser mimo achievable rates with downlink training and channel state feedback":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "beyond dirty paper coding for multi antenna broadcast channel with partial csit a rate splitting approach")

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.NI(Networking and Internet Architecture)