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: "on energy efficient noma designs for heterogeneous low latency downlink transmissions"
  Abstract: This paper investigates energy-efficient resource allocation for the two-user downlink with strict latency constraints at users. To cope with strict latency constraints, the capacity formula of the finite blocklength codes (FBCs) is adopted, in contrast to the classical Shannon capacity formula. The FBC formula explicitly specifies the trade-off between blocklength and reliability. We first consider the case where the transmitter uses super-position coding based non-orthogonal multiple access (NOMA). However, due to heterogeneous latency constraints and channel conditions at users, the conventional successive interference cancellation may be infeasible. We thus propose to use different interference mitigation schemes according to heterogeneous user conditions and solve the corresponding NOMA design problems. Though the target energy function is non-convex and implicit, optimal user blocklength and power allocation can still be identified for the considered NOMA schemes and checking the problem feasibility is simple. It is observed that when the latency requirements are more heterogeneous, the NOMA scheme cannot achieve the best energy-efficiency of the downlink. In view of this, a hybrid transmission scheme which includes time division multiple access (TDMA) and NOMA as special cases is considered. Although the energy minimization is even challenging than the pure NOMA design problem, we propose a concave approximation of the FBC capacity formula which allows to obtain computationally efficient and high-quality solutions. Simulation results show that the hybrid scheme can benefits from both NOMA and TDMA.

Connected through hyperedge HE-1 (induced by source paper "harq cc enabled noma designs with outage probability constraints"):
  with members:
    - "on the performance of noma with hybrid arq"
    - "a survey on non orthogonal multiple access for 5g networks research challenges and future trends"
    - "joint beamforming and power splitting control in downlink cooperative swipt noma systems"
    - "sub channel assignment power allocation and user scheduling for non orthogonal multiple access networks"
    - "what will 5g be"
    - "on the performance of non orthogonal multiple access in 5g systems with randomly deployed users"
    - "coordinated beamforming for multiuser miso interference channel under rate outage constraints"
    - "on the complexity of joint subcarrier and power allocation for multi user ofdma 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)