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: "modeling and analysis of uplink non orthogonal multiple access noma in large scale cellular networks using poisson cluster processes"
  Abstract: Non-orthogonal multiple access (NOMA) serves multiple users by superposing their distinct message signals. The desired message signal is decoded at the receiver by applying successive interference cancellation (SIC). Using the theory of Poisson cluster process (PCP), we provide a framework to analyze multi-cell uplink NOMA systems. Specifically, we characterize the rate coverage probability of a NOMA user who is at rank $m$ (in terms of the distance from its serving BS) among all users in a cell and the mean rate coverage probability of all users in a cell. Since the signal-to-interference-plus-noise ratio (SINR) of $m$-th user relies on efficient SIC, we consider three scenarios, i.e., perfect SIC (in which the signals of $m-1$ interferers who are stronger than $m$-th user are decoded successfully), imperfect SIC (in which the signals of of $m-1$ interferers who are stronger than $m$-th user may or may not be decoded successfully), and imperfect worst case SIC (in which the decoding of the signal of $m$-th user is always unsuccessful whenever the decoding of its relative $m-1$ stronger users is unsuccessful). The derived expressions are customized to capture the performance of a user at rank $m$ in an equivalent orthogonal multiple access (OMA) system. Finally, numerical results are presented to validate the derived expressions.

Connected through hyperedge HE-1 (induced by source paper "non orthogonal multiple access for 5g and beyond"):
  with members:
    - "downlink non orthogonal multiple access with limited feedback"
    - "optimal user scheduling and power allocation for millimeter wave noma systems"
    - "coordinated multi point comp transmission in downlink multi cell noma systems models and spectral efficiency performance"
    - "low complexity receiver for multi level polar coded modulation in non orthogonal multiple access"
    - "massive non orthogonal multiple access for cellular iot potentials and limitations"
    - "power domain non orthogonal multiple access noma in 5g systems potentials and challenges"
    - "modulation and multiple access for 5g networks"
    - "robust beamforming techniques for non orthogonal multiple access systems with bounded channel uncertainties"

Connected through hyperedge HE-2 (induced by source paper "performance analysis of noma with fixed gain relaying over nakagami m fading channels"):
  with members:
    - "nonorthogonal multiple access in large scale underlay cognitive radio networks"
    - "cooperative non orthogonal multiple access with simultaneous wireless information and power transfer"
    - "fairness for non orthogonal multiple access in 5g systems"
    - "on the performance of non orthogonal multiple access in 5g systems with randomly deployed users"

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)