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: "enhancing performance of random caching in large scale wireless networks with multiple receive antennas"
  Abstract: To improve signal-to-interference ratio (SIR) and make better use of file diversity provided by random caching, we consider two types of linear receivers, i.e., maximal ratio combining (MRC) receiver and partial zero forcing (PZF) receiver, at users in a large-scale cache-enabled single-input multi-output (SIMO) network. First, for each receiver, by utilizing tools from stochastic geometry, we derive a tractable expression and a tight upper bound for the successful transmission probability (STP). In the case of the MRC receiver, we also derive a closed-form expression for the asymptotic outage probability in the low SIR threshold regime. Then, for each receiver, we maximize the STP. In the case of the MRC receiver, we consider the maximization of the tight upper bound on the STP by optimizing the caching distribution, which is a non-convex problem. We obtain a stationary point, by solving an equivalent difference of convex (DC) programming problem using concave-convex procedure (CCCP). We also obtain a closed-form asymptotically optimal solution in the low SIR threshold regime. In the case of the PZF receiver, we consider the maximization of the tight upper bound on the STP by optimizing the caching distribution and the degrees of freedom (DoF) allocation (for boosting the signal power), which is a mixed discrete-continuous problem. Based on structural properties, we obtain a low-complexity near optimal solution by using an alternating optimization approach. The analysis and optimization results reveal the impact of antenna resource at users on random caching. Finally, by numerical results, we show that the random caching design with the PZF receiver achieves significant performance gains over the random caching design with the MRC receiver and some baseline caching designs.

Connected through hyperedge HE-1 (induced by source paper "enhancing physical layer security of random caching in large scale multi antenna heterogeneous wireless networks"):
  with members:
    - "secure and energy efficient transmissions in cache enabled heterogeneous cellular networks performance analysis and optimization"
    - "physical layer security in cache enabled cooperative small cell networks against randomly distributed eavesdroppers"
    - "a unified framework for the tractable analysis of multi antenna wireless networks"
    - "enhancing performance of random caching in large scale heterogeneous wireless networks with random discontinuous transmission"
    - "delivery secrecy tradeoff for cache enabled stochastic networks content placement optimization"
    - "random caching based cooperative transmission in heterogeneous wireless networks"
    - "caching at the wireless edge design aspects challenges and future directions"
    - "optimal content placement for offloading in cache enabled heterogeneous wireless networks"

Connected through hyperedge HE-2 (induced by source paper "enhancing performance of random caching in large scale heterogeneous wireless networks with random discontinuous transmission"):
  with members:
    - "random caching based cooperative transmission in heterogeneous wireless networks"
    - "spatio temporal interference correlation and joint coverage in cellular networks"
    - "caching at the wireless edge design aspects challenges and future directions"
    - "optimal content placement for offloading in cache enabled heterogeneous wireless networks"
    - "effect of retransmissions on optimal caching in cache enabled small cell networks"
    - "wireless caching technical misconceptions and business barriers"
    - "a learning based approach to caching in heterogenous small cell networks"
    - "cache enabled heterogeneous cellular networks comparison and tradeoffs"

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)