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: "gaussian message passing for overloaded massive mimo noma"
  Abstract: This paper considers a low-complexity Gaussian Message Passing (GMP) scheme for a coded massive Multiple-Input Multiple-Output (MIMO) systems with Non-Orthogonal Multiple Access (massive MIMO-NOMA), in which a base station with $N_s$ antennas serves $N_u$ sources simultaneously in the same frequency. Both $N_u$ and $N_s$ are large numbers, and we consider the overloaded cases with $N_u>N_s$. The GMP for MIMO-NOMA is a message passing algorithm operating on a fully-connected loopy factor graph, which is well understood to fail to converge due to the correlation problem. In this paper, we utilize the large-scale property of the system to simplify the convergence analysis of the GMP under the overloaded condition. First, we prove that the \emph{variances} of the GMP definitely converge to the mean square error (MSE) of Linear Minimum Mean Square Error (LMMSE) multi-user detection. Secondly, the \emph{means} of the traditional GMP will fail to converge when $ N_u/N_s 1$, and show that it has a faster convergence speed than the traditional GMP with the same complexity. Finally, numerical results are provided to verify the validity and accuracy of the theoretical results presented.

Connected through hyperedge HE-1 (induced by source paper "code domain noma in massive mimo when is it needed"):
  with members:
    - "what is the benefit of code domain noma in massive mimo"
    - "what role can noma play in massive mimo"
    - "massive mimo has unlimited capacity"
    - "massive mimo performance tdd versus fdd what do measurements say"
    - "power domain non orthogonal multiple access noma in 5g systems potentials and challenges"
    - "fundamental limits of low density spreading noma with fading"
    - "massive mimo performance evaluation based on measured propagation data"
    - "a coordinated approach to channel estimation in large scale multiple antenna systems"

Connected through hyperedge HE-2 (induced by source paper "interplay between noma and other emerging technologies a survey"):
  with members:
    - "secure relaying in non orthogonal multiple access trusted and untrusted scenarios"
    - "securing downlink massive mimo noma networks with artificial noise"
    - "lens based millimeter wave reconfigurable antenna noma"
    - "what role can noma play in massive mimo"
    - "noma an information theoretic perspective"
    - "joint power and time allocation for noma mec offloading"
    - "multi beam noma for hybrid mmwave systems"
    - "exploiting full half duplex user relaying in noma systems"

Connected through hyperedge HE-3 (induced by source paper "decentralized equalization with feedforward architectures for massive mu mimo"):
  with members:
    - "feedforward architectures for decentralized precoding in massive mu mimo systems"
    - "efficient dsp and circuit architectures for massive mimo state of the art and future directions"
    - "decentralized baseband processing for massive mu mimo systems"
    - "optimally tuned nonparametric linear equalization for massive mu mimo systems"
    - "on the achievable rates of decentralized equalization in massive mu mimo systems"
    - "on the performance of mismatched data detection in large mimo systems"
    - "performance trade offs in multi processor approximate message passing"
    - "the world s first real time testbed for massive mimo design implementation and validation"

Connected through hyperedge HE-4 (induced by source paper "a new insight into gamp and amp"):
  with members:
    - "capacity achieving mimo noma iterative lmmse detection"
    - "capacity optimality of amp in coded systems"
    - "a unified bayesian inference framework for generalized linear models"
    - "bilinear adaptive generalized vector approximate message passing"
    - "massive connectivity with massive mimo part i device activity detection and channel estimation"
    - "beam on graph simultaneous channel estimation for mmwave mimo systems with multiple users"
    - "rigorous dynamics of expectation propagation based signal recovery from unitarily invariant measurements"
    - "hybrid approximate message passing"

Connected through hyperedge HE-5 (induced by source paper "what is the benefit of code domain noma in massive mimo"):
  with members:
    - "what role can noma play in massive mimo"
    - "massive mimo has unlimited capacity"
    - "fundamental limits of low density spreading noma with fading"

Connected through hyperedge HE-6 (induced by source paper "rate splitting for multi antenna non orthogonal unicast and multicast transmission spectral and energy efficiency analysis"):
  with members:
    - "energy efficiency of rate splitting multiple access and performance benefits over sdma and noma"
    - "rate splitting multiple access for coordinated multi point joint transmission"
    - "rate splitting for multi antenna non orthogonal unicast and multicast transmission"
    - "rate splitting multiple access for downlink communication systems bridging generalizing and outperforming sdma and noma"
    - "rate splitting for max min fair multigroup multicast beamforming in overloaded systems"
    - "optimal dof region of the k user miso bc with partial csit"
    - "generalized multicast multibeam precoding for satellite communications"
    - "energy efficient joint unicast and multicast beamforming with multi antenna user terminals"

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)