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: "non orthogonal multiple access with orthogonal time frequency space signal transmission"
  Abstract: Orthogonal time frequency space (OTFS) is being pursued in recent times as a suitable wireless transmission technology for use in high mobility scenarios. In this work, we propose nonorthogonal multiple acess (NOMA) based OTFS which may be called NOMA-OTFS system and evaluate its performance from system level and link level perspective. The challenge lies in the fact that while OTFS transmission technology is known for its resilience to high mobility conditions, while NOMA is known to yield high spectral efficiency in low mobility scenarios in comparison to orthogonal multiple access (OMA). We present a minimum mean square error (MMSE)- successive interference cancellation (SIC) based receiver for NOMA-OTFS, for which we derive expression for symbol-wise post-processing SINR in order to evaluate system sum spectral efficiency (SE). We develop power allocation schemes to maximize the sum SE in the high-mobility version of NOMA. We further design a realizable codeword level SIC (CWIC) receiver using LDPC codes along with MMSE equalization for evaluating link level performance of such practical NOMA-OTFS system. The system level and link level performance of the proposed NOMA-OTFS system are compared against benchmark OMA-OTFS, OMA-orthogonal frequency division multiplexing (OMA-OFDM) and NOMA-OFDM schemes. From system-level performance evaluation, we observe interestingly that NOMA-OTFS provides higher sum SE than OMA-OTFS. When compared to NOMA-OFDM, we find that outage SE of NOMA-OTFS is improved at the cost of decrease in mean SE. Whereas link-level results show that the developed CWIC based NOMA-OTFS receiver performs significantly better than NOMA-OFDM in terms of block error rate (BLER), goodput and throughput.

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)