human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "blind signal classification for non orthogonal multiple access in internet of things networks"):
  Abstract: For downlink multiple-user (MU) transmission based on non-orthogonal multiple access (NOMA), the advanced receiver strategy is required to cancel the inter-user interference, e.g., successive interference cancellation (SIC). The SIC process can be applicable only when information about the co-scheduled signal is known at the user terminal (UT) side. In particular, the UT should know whether the received signal is OMA or NOMA, whether SIC is required or not, and which modulation orders and power ratios have been used for the superposed UTs, before decoding the signal. An efficient network, e.g., Internet-of-Things (IoT), requires that UT blindly classify the received signal and apply a matching receiver strategy to reduce the high-layer signaling overhead and to increase resource efficiency. In this paper, we first analyze the performance impact of errors in NOMA signal classification and address ensuing receiver challenges in practical IoT-based MU usage cases. In order to reduce the blind signal classification error, we propose transmission schemes that rotate data symbols or pilots to a specific phase according to the transmitted signal format. In the case of pilot rotation, a new signal classification algorithm is also proposed. The performance improvements by the proposed methods are verified by intensive simulation results.

Includes members:
  Member "bi directional cooperative noma without full csit":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "blind signal classification analysis and impact on user scheduling and power allocation in nonorthogonal multiple access")
  Member "deep learning models for wireless signal classification with distributed low cost spectrum sensors":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a survey on machine learning based performance improvement of wireless networks phy mac and network layer")
  Member "deep architectures for modulation recognition":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a survey on machine learning based performance improvement of wireless networks phy mac and network layer")
  Member "v2x meets noma non orthogonal multiple access for 5g enabled vehicular networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a survey of rate optimal power domain noma schemes for enabling technologies of future wireless networks")
  Member "noma based calibration for large scale spaceborne antenna arrays":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "blind signal classification for non orthogonal multiple access in vehicular networks")
  Member "a general mimo framework for noma downlink and uplink transmission based on signal alignment":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "mimo noma networks relying on reconfigurable intelligent surface a signal cancellation based design")

Question: Please tell me which class the source paper 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)