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: "practical power balanced non orthogonal multiple access"
  Abstract: This paper investigates practical 5G strategies for power-balanced non-orthogonal multiple access (NOMA). By allowing multiple users to share the same time and frequency, NOMA can scale up the number of served users and increase spectral efficiency compared with existing orthogonal multiple access (OMA). Conventional NOMA schemes with successive interference cancellation (SIC) do not work well when users with comparable received powers transmit together. To allow power-balanced NOMA (more exactly, near power-balanced NOMA), this paper investigates a new NOMA architecture, named Network-Coded Multiple Access (NCMA). A distinguishing feature of NCMA is the joint use of physical-layer network coding (PNC) and multiuser decoding (MUD) to boost NOMA throughputs. We first show that a simple NCMA architecture in which all users use the same modulation, referred to as rate-homogeneous NCMA, can achieve substantial throughput improvement over SIC-based NOMA under near power-balanced scenarios. Then, we put forth a new NCMA architecture, referred to as rate-diverse NCMA, in which different users may adopt different modulations commensurate with their relative SNRs. A challenge for rate-diverse NCMA is the design of a channel-coded PNC system. This paper is the first attempt to design channel-coded rate-diverse PNC. Experimental results on our software-defined radio prototype show that the throughput of rate-diverse NCMA can outperform the state-of-the-art rate-homogeneous NCMA by 80%. Overall, rate-diverse NCMA is a practical solution for near power-balanced NOMA.

Connected through hyperedge HE-1 (induced by source paper "coherent detection for short packet physical layer network coding with fsk modulation"):
  with members:
    - "noncoherent detection for physical layer network coding"
    - "ultra reliable short packet communications half duplex or full duplex relaying"
    - "complex linear physical layer network coding"
    - "mobile lattice coded physical layer network coding with practical channel alignment"
    - "on the subtleties of q pam linear physical layer network coding"
    - "reliable physical layer network coding supporting real applications"
    - "ultra reliable communication in 5g wireless systems"
    - "physical layer network coding tutorial survey and beyond"

Connected through hyperedge HE-2 (induced by source paper "a low complexity recursive approach toward code domain noma for massive communications"):
  with members:
    - "a survey on non orthogonal multiple access for 5g networks research challenges and future trends"
    - "lattice partition multiple access a new method of downlink non orthogonal multiuser transmissions"
    - "on the performance of non orthogonal multiple access in 5g systems with randomly deployed users"
    - "compound polar codes"

Connected through hyperedge HE-3 (induced by source paper "a survey on non orthogonal multiple access for 5g networks research challenges and future trends"):
  with members:
    - "exploiting multiple antenna techniques for non orthogonal multiple access"
    - "achievable rate region of non orthogonal multiple access systems with wireless powered decoder"
    - "a new view of multi user hybrid massive mimo non orthogonal angle division multiple access"
    - "on non orthogonal multiple access with finite alphabet inputs in z channels"
    - "spectrum and energy efficient beamspace mimo noma for millimeter wave communications using lens antenna array"
    - "noma based random access with multichannel aloha"
    - "optimal power allocation scheme for non orthogonal multiple access with alpha fairness"
    - "on optimal power allocation for downlink non orthogonal multiple access systems"

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)