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: "hybrid precoder design for cache enabled millimeter wave radio access networks"
  Abstract: In this paper, we study the design of a hybrid precoder, consisting of an analog and a digital precoder, for the delivery phase of downlink cache-enabled millimeter wave (mmWave) radio access networks (CeMm-RANs). In CeMm-RANs, enhanced remote radio heads (eRRHs), which are equipped with local cache and baseband signal processing capabilities in addition to the basic functionalities of conventional RRHs, are connected to the baseband processing unit via fronthaul links. Two different fronthaul information transfer strategies are considered, namely, hard fronthaul information transfer, where hard information of uncached requested files is transmitted via the fronthaul links to a subset of eRRHs, and soft fronthaul information transfer, where the fronthaul links are used to transmit quantized baseband signals of uncached requested files. The hybrid precoder is optimized for maximization of the minimum user rate under a fronthaul capacity constraint, an eRRH transmit power constraint, and a constant-modulus constraint on the analog precoder. The resulting optimization problem is non-convex, and hence the global optimal solution is difficult to obtain. Therefore, convex approximation methods are employed to tackle the non-convexity of the achievable user rate, the fronthaul capacity constraint, and the constant modulus constraint on the analog precoder. Then, an effective algorithm with provable convergence is developed to solve the approximated optimization problem. Simulation results are provided to evaluate the performance of the proposed algorithms, where fully digital precoding is used as benchmark. The results reveal that except for the case of a large fronthaul link capacity, soft fronthaul information transfer is preferable for CeMm-RANs.

Connected through hyperedge HE-1 (induced by source paper "edge cache assisted secure low latency millimeter wave transmission"):
  with members:
    - "securing downlink massive mimo noma networks with artificial noise"
    - "secure and energy efficient transmissions in cache enabled heterogeneous cellular networks performance analysis and optimization"
    - "two level transmission scheme for cache enabled fog radio access networks"
    - "optimized base station cache allocation for cloud radio access network with multicast backhaul"
    - "exploiting physical layer security for multiuser multicarrier computation offloading"
    - "content placement in cache enabled sub 6 ghz and millimeter wave multi antenna dense small cell networks"
    - "random caching based cooperative transmission in heterogeneous wireless networks"
    - "cache enabled physical layer security for video streaming in backhaul limited cellular networks"

Connected through hyperedge HE-2 (induced by source paper "cloud edge coordinated processing low latency multicasting transmission"):
  with members:
    - "two level transmission scheme for cache enabled fog radio access networks"
    - "iteratively linearized reweighted alternating direction method of multipliers for a class of nonconvex problems"
    - "content placement in cache enabled sub 6 ghz and millimeter wave multi antenna dense small cell networks"
    - "air ground integrated vehicular network slicing with content pushing and caching"
    - "distributed optimization for coordinated beamforming in multicell multigroup multicast systems power minimization and sinr balancing"
    - "fog aided wireless networks for content delivery fundamental latency tradeoffs"
    - "random caching based cooperative transmission in heterogeneous wireless networks"
    - "cache enabled physical layer security for video streaming in backhaul limited cellular 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)