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 "mobile edge caching an optimal auction approach"):
  Abstract: With the explosive growth of wireless data, the sheer size of the mobile traffic is challenging the capacity of current wireless systems. To tackle this challenge, mobile edge caching has emerged as a promising paradigm recently, in which the service providers (SPs) prefetch some popular contents in advance and cache them locally at the network edge. When requested, those locally cached contents can be directly delivered to users with low latency, thus alleviating the traffic load over backhaul channels during peak hours and enhancing the quality-of-experience (QoE) of users simultaneously. Due to the limited available cache space, it makes sense for the SP to cache the most profitable contents. Nevertheless, users' true valuations of contents are their private knowledge, which is unknown to the SP in general. This information asymmetry poses a significant challenge for effective caching at the SP side. Further, the cached contents can be delivered with different quality, which needs to be chosen judiciously to balance delivery costs and user satisfaction. To tackle these difficulties, in this paper, we propose an optimal auction mechanism from the perspective of the SP. In the auction, the SP determines the cache space allocation over contents and user payments based on the users' (possibly untruthful) reports of their valuations so that the SP's expected revenue is maximized. The advocated mechanism is designed to elicit true valuations from the users (incentive compatibility) and to incentivize user participation (individual rationality). In addition, we devise a computationally efficient method for calculating the optimal cache space allocation and user payments. We further examine the optimal choice of the content delivery quality for the case with a large number of users and derive a closed-form solution to compute the optimal delivery quality.

Includes members:
  Member "fundamental tradeoff between storage and latency in cache aided wireless interference networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "fundamental limits of wireless caching under mixed cacheable and uncacheable traffic")
  Member "how much cache is needed to achieve linear capacity scaling in backhaul limited dense wireless networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "edge caching in dense heterogeneous cellular networks with massive mimo aided self backhaul")
  Member "pricing and resource allocation via game theory for a small cell video caching system":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a crowdsourcing framework for on device federated learning")
  Member "information theoretic caching the multi user case":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on the optimality of d2d coded caching with uncoded cache placement and one shot delivery")
  Member "caching at the wireless edge design aspects challenges and future directions":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "enhancing physical layer security of random caching in large scale multi antenna heterogeneous wireless networks")
  Member "joint caching and pricing strategies for popular content in information centric networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "cost sharing games for energy efficient multi hop broadcast in wireless networks")
  Member "wireless content caching for small cell and d2d networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a unified framework for constructing centralized coded caching schemes")
  Member "a rate distortion approach to caching":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "rate distortion memory trade offs in heterogeneous caching 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.NI(Networking and Internet Architecture)