human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "spatial alignment of coding and modulation helps content delivery":
    also connected through other hyperedges:
      (none)
  Member "caching policy toward maximal success probability and area spectral efficiency of cache enabled hetnets":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the design of dynamic probabilistic caching with time varying content popularity")
  Member "random caching in backhaul limited multi antenna networks analysis and area spectrum efficiency optimization":
    also connected through other hyperedges:
      (none)
  Member "temporal spatial aggregation for cache enabled wireless multicasting networks with asynchronous content requests":
    also connected through other hyperedges:
      (none)
  Member "joint and competitive caching designs in large scale multi tier wireless multicasting networks":
    also connected through other hyperedges:
      (none)
  Member "wireless caching technical misconceptions and business barriers":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "fundamental limits of wireless caching under mixed cacheable and uncacheable traffic")
  Member "a learning based approach to caching in heterogenous small cell networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "online convex optimization for caching networks")
  Member "analysis and optimization of caching and multicasting in large scale cache enabled heterogeneous wireless networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "reinforcement learning based cooperative coded caching under dynamic popularities in ultra dense networks")

Question: Please tell me which class the source paper 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)