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: "noma based calibration for large scale spaceborne antenna arrays"
  Abstract: In the parallel calibration for transmitting phased arrays, the calibration receiver must separate the signals belonging to different antenna elements to avoid mutual interference. Existing algorithms encode different antenna elements' radiation with orthogonal signature codes, but these algorithms are far from desired for large-scale spaceborne antenna arrays. Considering the strictly limited resources on satellites, to improve hardware efficiency of large-scale spaceborne antenna arrays, in this work inspired by the idea of non-orthogonal multiple access (NOMA) we design a series of non-orthogonal signature codes for different antenna elements by Cyclically Shifting an m-Sequence (CSmS) with different offsets named as CSmS-NOMA signaling. This design can strike an elegant balance between the performance and complexity and is very suitable for large-scale spaceborne antenna arrays. It is shown that no matter how many antenna elements there are to be calibrated simultaneously, CSmS-NOMA signaling needs only one calibrating waveform generator and one matched filter. Hence it is much more efficient than the existing fully orthogonal schemes. In order to evaluate the achievable calibration accuracy, a unified theoretical framework is developed based on which the relationship between calibration accuracy and signal to noise ratio (SNR) has been clearly revealed. Furthermore, a hardware experiment platform is also built to assess the theoretical work. For all the considered scenarios, it can be concluded that the theoretical, simulated and experimental results coincide with each other perfectly.

Connected through hyperedge HE-1 (induced by source paper "blind signal classification for non orthogonal multiple access in vehicular networks"):
  with members:
    - "blind signal classification analysis and impact on user scheduling and power allocation in nonorthogonal multiple access"
    - "bi directional cooperative noma without full csit"
    - "deep learning models for wireless signal classification with distributed low cost spectrum sensors"
    - "power domain non orthogonal multiple access noma in 5g systems potentials and challenges"
    - "deep architectures for modulation recognition"
    - "v2x meets noma non orthogonal multiple access for 5g enabled vehicular networks"
    - "a general mimo framework for noma downlink and uplink transmission based on signal alignment"
    - "on the performance of non orthogonal multiple access in 5g systems with randomly deployed users"

Connected through hyperedge HE-2 (induced by source paper "blind signal classification for non orthogonal multiple access in internet of things networks"):
  with members:
    - "bi directional cooperative noma without full csit"
    - "deep learning models for wireless signal classification with distributed low cost spectrum sensors"
    - "deep architectures for modulation recognition"
    - "v2x meets noma non orthogonal multiple access for 5g enabled vehicular networks"
    - "a general mimo framework for noma downlink and uplink transmission based on signal alignment"

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)