human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "max min fair transmit precoding for multi group multicasting in massive mimo"
  Abstract: This paper considers the downlink precoding for physical layer multicasting in massive multiple-input-multiple-output (MIMO) systems. We study the max-min fairness (MMF) problem, where channel state information (CSI) at the transmitter is used to design precoding vectors that maximize the minimum spectral efficiency (SE) of the system, given fixed power budgets for uplink training and downlink transmission. Our system model accounts for channel estimation, pilot contamination, arbitrary pathlosses, and multi-group multicasting. We consider six scenarios with different transmission technologies (unicast and multicast), different pilot assignment strategies (dedicated or shared pilot assignments), and different precoding schemes (maximum ratio transmission and zero forcing), and derive achievable spectral efficiencies for all possible combinations. Then we solve the MMF problem for each of these scenarios and for any given pilot length we find the SE maximizing uplink pilot and downlink data transmission policies, all in closed-forms. We use these results to draw a general guideline for massive MIMO multicasting design, where for a given number of base station antennas, number of users, and coherence interval length, we determine the multicasting scheme that shall be used.

Connected through hyperedge HE-1 (induced by source paper "joint annotator and spectrum allocation in wireless networks for crowd labelling"):
  with members:
    - "machine learning at the wireless edge distributed stochastic gradient descent over the air"
    - "federated learning via over the air computation"
    - "towards an intelligent edge wireless communication meets machine learning"
    - "wirelessly powered crowd sensing joint power transfer sensing compression and transmission"
    - "on the spectral efficiency and security enhancements of noma assisted multicast unicast streaming"
    - "content centric sparse multicast beamforming for cache enabled cloud ran"
    - "optimal dynamic multicast scheduling for cache enabled content centric wireless networks"

Connected through hyperedge HE-2 (induced by source paper "max min fair sensor scheduling game theoretic perspective and algorithmic solution"):
  with members:
    - "attack allocation on remote state estimation in multi systems structural results and asymptotic solution"
    - "sensor scheduling in variance based event triggered estimation with packet drops"
    - "an axiomatic theory of fairness in network resource allocation"

Connected through hyperedge HE-3 (induced by source paper "joint unicast and multi group multicast transmission in massive mimo systems"):
  with members:
    - "large system analysis of power normalization techniques in massive mimo"
    - "hybrid precoding for multi group physical layer multicasting"
    - "multigroup multicast precoding in massive mimo"
    - "reducing the computational complexity of multicasting in large scale antenna systems"
    - "joint beamforming and broadcasting in massive mimo"
    - "hybrid precoding for physical layer multicasting"
    - "on the spectral efficiency and security enhancements of noma assisted multicast unicast streaming"
    - "massive mimo ten myths and one critical question"

Question: Please tell me which class the center node 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)