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: "exploiting mobility in cache assisted d2d networks performance analysis and optimization"
  Abstract: Caching popular content at mobile devices, accompanied by device-to-device (D2D) communications, is one promising technology for effective mobile content delivery. User mobility is an important factor when investigating such networks, which unfortunately was largely ignored in most previous works. Preliminary studies have been carried out, but the effect of mobility on the caching performance has not been fully understood. In this paper, by explicitly considering users' contact and inter-contact durations via an alternating renewal process, we first investigate the effect of mobility with a given cache placement. A tractable expression of the data offloading ratio, i.e., the proportion of requested data that can be delivered via D2D links, is derived, which is proved to be increasing with the user moving speed. The analytical results are then used to develop an effective mobility-aware caching strategy to maximize the data offloading ratio. Simulation results are provided to confirm the accuracy of the analytical results and also validate the effect of user mobility. Performance gains of the proposed mobility-aware caching strategy are demonstrated with both stochastic models and real-life data sets. It is observed that the information of the contact durations is critical to design cache placement, especially when they are relatively short or comparable to the inter-contact durations.

Connected through hyperedge HE-1 (induced by source paper "mobile edge intelligence and computing for the internet of vehicles"):
  with members:
    - "edge intelligence paving the last mile of artificial intelligence with edge computing"
    - "client edge cloud hierarchical federated learning"
    - "a mobility aware vehicular caching scheme in content centric networks model and optimization"
    - "edge assisted hierarchical federated learning with non iid data"
    - "cavbench a benchmark suite for connected and autonomous vehicles"
    - "federated learning strategies for improving communication efficiency"
    - "edge intelligence on demand deep learning model co inference with device edge synergy"
    - "follow me at the edge mobility aware dynamic service placement for mobile edge computing"

Connected through hyperedge HE-2 (induced by source paper "mobility aware content preference learning in decentralized caching networks"):
  with members:
    - "decentralized multi task learning based on extreme learning machines"
    - "user preference learning based edge caching for fog radio access network"
    - "learning to reweight examples for robust deep learning"
    - "mobility and popularity aware coded small cell caching"
    - "caching policy for cache enabled d2d communications by learning user preference"
    - "context aware proactive content caching with service differentiation in wireless networks"
    - "optimizing content caching to maximize the density of successful receptions in device to device networking"
    - "effect of user mobility on the performance of device to device networks with distributed caching"

Connected through hyperedge HE-3 (induced by source paper "mobility aware resource allocation in vlc networks using t step look ahead policy"):
  with members:
    - "mobility aware caching in d2d networks"
    - "a survey of energy efficient techniques for 5g networks and challenges ahead"
    - "joint link scheduling and brightness control for greening vlc based indoor access 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)