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: "low complexity and high resolution doa estimation for hybrid analog and digital massive mimo receive array"
  Abstract: A large-scale fully-digital receive antenna array can provide very high-resolution direction of arrival (DOA) estimation, but resulting in a significantly high RF-chain circuit cost. Thus, a hybrid analog and digital (HAD) structure is preferred. Two phase alignment (PA) methods, HAD PA (HADPA) and hybrid digital and analog PA (HDAPA), are proposed to estimate DOA based on the parametric method. Compared to analog phase alignment (APA), they can significantly reduce the complexity in the PA phases. Subsequently, a fast root multiple signal classification HDAPA (Root-MUSIC-HDAPA) method is proposed specially for this hybrid structure to implement an approximately analytical solution. Due to the HAD structure, there exists the effect of direction-finding ambiguity. A smart strategy of maximizing the average receive power is adopted to delete those spurious solutions and preserve the true optimal solution by linear searching over a set of limited finite candidate directions. This results in a significant reduction in computational complexity. Eventually, the Cramer-Rao lower bound (CRLB) of finding emitter direction using the HAD structure is derived. Simulation results show that our proposed methods, Root-MUSIC-HDAPA and HDAPA, can achieve the hybrid CRLB with their complexities being significantly lower than those of pure linear searching-based methods, such as APA.

Connected through hyperedge HE-1 (induced by source paper "energy efficient alternating iterative secure structure of maximizing secrecy rate for directional modulation networks"):
  with members:
    - "alternating iterative secure structure between beamforming and power allocation for uav aided directional modulation networks"
    - "robust secure transmission of using main lobe integration based leakage beamforming in directional modulation mu mimo systems"
    - "secure transmission with large numbers of antennas and finite alphabet inputs"
    - "artificial noise aided secure multicast precoding for directional modulation systems"
    - "artificial noise aided secure transmission with directional modulation based on random frequency diverse arrays"
    - "optimal power allocation for artificial noise under imperfect csi against spatially random eavesdroppers"
    - "an overview of sustainable green 5g networks"
    - "secure massive mimo transmission in the presence of an active eavesdropper"

Connected through hyperedge HE-2 (induced by source paper "optimal power allocation for secure directional modulation networks with a full duplex uav user"):
  with members:
    - "robust secure transmission of using main lobe integration based leakage beamforming in directional modulation mu mimo systems"
    - "secure uav communication with cooperative jamming and trajectory control"
    - "secure transmission with large numbers of antennas and finite alphabet inputs"
    - "joint trajectory and communication design for multi uav enabled wireless networks"
    - "securing uav communications via trajectory optimization"
    - "wireless communications with unmanned aerial vehicles opportunities and challenges"
    - "relay selection improves the security reliability trade off in cognitive radio systems"

Connected through hyperedge HE-3 (induced by source paper "alternating iterative secure structure between beamforming and power allocation for uav aided directional modulation networks"):
  with members:
    - "robust secure transmission of using main lobe integration based leakage beamforming in directional modulation mu mimo systems"
    - "secure transmission with large numbers of antennas and finite alphabet inputs"
    - "joint trajectory and communication design for multi uav enabled wireless networks"
    - "artificial noise aided secure multicast precoding for directional modulation systems"
    - "interference alignment and soft space reuse based cooperative transmission for multi cell massive mimo networks"
    - "overcoming endurance issue uav enabled communications with proactive caching"
    - "energy efficient uav communication with trajectory optimization"
    - "artificial noise aided secure transmission with directional modulation based on random frequency diverse arrays"

Connected through hyperedge HE-4 (induced by source paper "an efficient hybrid beamforming design for massive mimo receive systems via sinr maximization based on an improved bat algorithm"):
  with members:
    - "spatially sparse precoding in millimeter wave mimo systems"

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)