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: "bi directional cooperative noma without full csit"
  Abstract: In this paper, we propose bi-directional cooperative non-orthogonal multiple access (NOMA). Compared to conventional NOMA, the main contributions of bi-directional cooperative NOMA can be explained in two directions: 1) the proposed NOMA system is still efficient when the channel gains of scheduled users are almost the same and 2) the proposed NOMA system operates well without accurate channel-state information at the base station. In a two-user scenario, the closed-form ergodic capacity of bi-directional cooperative NOMA is derived, and it is proven to be better than those of other techniques. Based on the ergodic capacity, the algorithms to find optimal power allocations maximizing the user fairness and sum rate are presented. Outage probability is also derived, and we show that bi-directional cooperative NOMA achieves a power gain over uni-directional cooperative NOMA and a diversity gain over non-cooperative NOMA and orthogonal multiple access (OMA). We finally extend the bi-directional cooperative NOMA to a multiuser model. The analysis of ergodic capacity and outage probability in a two-user scenario is numerically verified. Also, simulation results show that bi-directional cooperative NOMA provides better data rates than the existing NOMA schemes as well as OMA in a multiuser scenario.

Connected through hyperedge HE-1 (induced by source paper "blind signal classification analysis and impact on user scheduling and power allocation in nonorthogonal multiple access"):
  with members:
    - "optimal user scheduling and power allocation for millimeter wave noma systems"
    - "on optimal power allocation for downlink non orthogonal multiple access systems"
    - "quadrature amplitude modulation division for multiuser miso broadcast channels"
    - "fairness for non orthogonal multiple access in 5g systems"
    - "a general mimo framework for noma downlink and uplink transmission based on signal alignment"
    - "what will 5g be"

Connected through hyperedge HE-2 (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"
    - "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"
    - "noma based calibration for large scale spaceborne antenna arrays"
    - "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-3 (induced by source paper "blind signal classification for non orthogonal multiple access in internet of things networks"):
  with members:
    - "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"
    - "noma based calibration for large scale spaceborne antenna arrays"
    - "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)