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: "symbol level and multicast precoding for multiuser multiantenna downlink a survey classification and challenges"
  Abstract: Precoding has been conventionally considered as an effective means of mitigating the interference and efficiently exploiting the available in the multiantenna downlink channel, where multiple users are simultaneously served with independent information over the same channel resources. The early works in this area were focused on transmitting an individual information stream to each user by constructing weighted linear combinations of symbol blocks (codewords). However, more recent works have moved beyond this traditional view by: i) transmitting distinct data streams to groups of users and ii) applying precoding on a symbol-per-symbol basis. In this context, the current survey presents a unified view and classification of precoding techniques with respect to two main axes: i) the switching rate of the precoding weights, leading to the classes of block- and symbol-level precoding, ii) the number of users that each stream is addressed to, hence unicast-/multicast-/broadcast- precoding. Furthermore, the classified techniques are compared through representative numerical results to demonstrate their relative performance and uncover fundamental insights. Finally, a list of open theoretical problems and practical challenges are presented to inspire further research in this area.

Connected through hyperedge HE-1 (induced by source paper "secure precise wireless transmission with random subcarrier selection based directional modulation transmit antenna array"):
  with members:
    - "covert communication in wireless relay networks"
    - "the random frequency diverse array a new antenna structure for uncoupled direction range indication in active sensing"
    - "artificial noise aided secure transmission in wiretap channels with transmitter side correlation"
    - "location based beamforming for enhancing secrecy in rician wiretap channels"
    - "directional modulation via symbol level precoding a way to enhance security"
    - "improving physical layer security in wireless communications using diversity techniques"
    - "relay selection improves the security reliability trade off in cognitive radio systems"
    - "optimization of code rates in sisome wiretap channels"

Connected through hyperedge HE-2 (induced by source paper "gpi based secrecy rate maximization beamforming scheme for wireless transmission with an aided directional modulation"):
  with members:
    - "robust secure transmission of using main lobe integration based leakage beamforming in directional modulation mu mimo systems"
    - "secure precise wireless transmission with random subcarrier selection based directional modulation transmit antenna array"
    - "artificial noise aided secure transmission in wiretap channels with transmitter side correlation"
    - "directional modulation via symbol level precoding a way to enhance security"
    - "secrecy transmit beamforming for heterogeneous networks"
    - "relay selection improves the security reliability trade off in cognitive radio systems"

Connected through hyperedge HE-3 (induced by source paper "artificial noise aided secure multicast precoding for directional modulation systems"):
  with members:
    - "artificial noise aided secure transmission in wiretap channels with transmitter side correlation"
    - "directional modulation via symbol level precoding a way to enhance security"
    - "relay selection improves the security reliability trade off in cognitive radio 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)