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: "regularized zero forcing precoding aided adaptive coding and modulation for large scale antenna array based air to air communications"
  Abstract: We propose a regularized zero-forcing transmit precoding (RZF-TPC) aided and distance-based adaptive coding and modulation (ACM) scheme to support aeronautical communication applications, by exploiting the high spectral efficiency of large-scale antenna arrays and link adaption. Our RZF-TPC aided and distance-based ACM scheme switches its mode according to the distance between the communicating aircraft. We derive the closed-form asymptotic signal-to-interference-plus-noise ratio (SINR) expression of the RZF-TPC for the aeronautical channel, which is Rician, relying on a non-centered channel matrix that is dominated by the deterministic line-of-sight component. The effects of both realistic channel estimation errors and of the co-channel interference are considered in the derivation of this approximate closed-form SINR formula. Furthermore, we derive the analytical expression of the optimal regularization parameter that minimizes the mean square detection error. The achievable throughput expression based on our asymptotic approximate SINR formula is then utilized as the design metric for the proposed RZF-TPC aided and distance-based ACM scheme. Monte-Carlo simulation results are presented for validating our theoretical analysis as well as for investigating the impact of the key system parameters. The simulation results closely match the theoretical results. In the specific example that two communicating aircraft fly at a typical cruising speed of 920 km/h, heading in opposite direction over the distance up to 740 km taking a period of about 24 minutes, the RZF-TPC aided and distance-based ACM is capable of transmitting a total of 77 Gigabyte of data with the aid of 64 transmit antennas and 4 receive antennas, which is significantly higher than that of our previous eigen-beamforming transmit precoding aided and distance-based ACM benchmark.

Connected through hyperedge HE-1 (induced by source paper "aeronautical ad hoc networking for the internet above the clouds"):
  with members:
    - "optimizing space air ground integrated networks by artificial intelligence"
    - "a survey of multi objective optimization in wireless sensor networks metrics algorithms and open problems"
    - "optical communication in space challenges and mitigation techniques"
    - "adaptive coding and modulation for large scale antenna array based aeronautical communications in the presence of co channel interference"
    - "survey of important issues in uav communication networks"
    - "on the security of the automatic dependent surveillance broadcast protocol"
    - "security challenges in mobile ad hoc networks a survey"
    - "massive mimo for next generation wireless systems"

Connected through hyperedge HE-2 (induced by source paper "boosting fronthaul capacity global optimization of power sharing for centralized radio access network"):
  with members:
    - "differential evolution algorithm aided turbo channel estimation and multi user detection for g fast systems in the presence of fext"
    - "joint precoding and rrh selection for user centric green mimo c ran"
    - "joint user selection and energy minimization for ultra dense multi channel c ran with incomplete csi"
    - "overview of millimeter wave communications for fifth generation 5g wireless networks with a focus on propagation models"
    - "adaptive coding and modulation for large scale antenna array based aeronautical communications in the presence of co channel interference"
    - "multivariate fronthaul quantization for downlink c ran"
    - "fronthauling for 5g lte u ultra dense cloud small cell networks"
    - "cluster content caching an energy efficient approach to improve quality of service in cloud radio access networks"

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)