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: "understanding performance of edge content caching for mobile video streaming"
  Abstract: Today's Internet has witnessed an increase in the popularity of mobile video streaming, which is expected to exceed 3/4 of the global mobile data traffic by 2019. To satisfy the considerable amount of mobile video requests, video service providers have been pushing their content delivery infrastructure to edge networks--from regional CDN servers to peer CDN servers (e.g., smartrouters in users' homes)--to cache content and serve users with storage and network resources nearby. Among the edge network content caching paradigms, Wi-Fi access point caching and cellular base station caching have become two mainstream solutions. Thus, understanding the effectiveness and performance of these solutions for large-scale mobile video delivery is important. However, the characteristics and request patterns of mobile video streaming are unclear in practical wireless network. In this paper, we use real-world datasets containing 50 million trace items of nearly 2 million users viewing more than 0.3 million unique videos using mobile devices in a metropolis in China over 2 weeks, not only to understand the request patterns and user behaviors in mobile video streaming, but also to evaluate the effectiveness of Wi-Fi and cellular-based edge content caching solutions. To understand performance of edge content caching for mobile video streaming, we first present temporal and spatial video request patterns, and we analyze their impacts on caching performance using frequency-domain and entropy analysis approaches. We then study the behaviors of mobile video users, including their mobility and geographical migration behaviors. Using trace-driven experiments, we compare strategies for edge content caching including LRU and LFU, in terms of supporting mobile video requests. Moreover, we design an efficient caching strategy based on the measurement insights and experimentally evaluate its performance.

Connected through hyperedge HE-1 (induced by source paper "pa cache learning based popularity aware content caching in edge networks"):
  with members:
    - "deep reinforcement learning for adaptive caching in hierarchical content delivery networks"
    - "online deep learning learning deep neural networks on the fly"
    - "what when and where to cache a unified optimization approach"
    - "optimizing timer based policies for general cache networks"
    - "caching at the wireless edge design aspects challenges and future directions"
    - "fractalnet ultra deep neural networks without residuals"
    - "mxnet a flexible and efficient machine learning library for heterogeneous distributed systems"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-2 (induced by source paper "caching at base stations with heterogeneous user demands and spatial locality"):
  with members:
    - "when exploiting individual user preference is beneficial for caching at base stations"
    - "optimal cache leasing from a mobile network operator to a content provider"
    - "3gpp inspired hetnet model using poisson cluster process sum product functionals and downlink coverage"
    - "caching policy optimization for d2d communications by learning user preference"
    - "modeling analysis and optimization of coded caching in small cell networks"
    - "wireless caching technical misconceptions and business barriers"
    - "big data caching for networking moving from cloud to edge"
    - "energy efficiency of downlink networks with caching at base stations"

Connected through hyperedge HE-3 (induced by source paper "fog computing applications taxonomy and requirements"):
  with members:
    - "all one needs to know about fog computing and related edge computing paradigms a complete survey"
    - "fog computing a taxonomy survey and future directions"
    - "fog computing survey of trends architectures requirements and research directions"
    - "a survey of state management in big data processing systems"
    - "foglearn leveraging fog based machine learning for smart system big data analytics"
    - "sdfog a software defined computing architecture for qos aware service orchestration over edge devices"
    - "decentralized computation offloading game for mobile cloud computing"
    - "long range communications in unlicensed bands the rising stars in the iot and smart city scenarios"

Connected through hyperedge HE-4 (induced by source paper "dynamic content updates in heterogeneous wireless networks"):
  with members:
    - "content popularity prediction towards location aware mobile edge caching"
    - "using grouped linear prediction and accelerated reinforcement learning for online content caching"
    - "optimal and scalable caching for 5g using reinforcement learning of space time popularities"
    - "caching with time varying popularity profiles a learning theoretic perspective"
    - "mobility and popularity aware coded small cell caching"
    - "context aware proactive content caching with service differentiation in wireless networks"
    - "learning based optimization of cache content in a small cell base station"

Connected through hyperedge HE-5 (induced by source paper "tile based joint caching and delivery of 360 o videos in heterogeneous networks"):
  with members:
    - "cost effective cache deployment in mobile heterogeneous networks"
    - "edge caching wireless networks performance analysis and optimization"
    - "multi stream switching for interactive virtual reality video streaming"
    - "caching at the wireless edge design aspects challenges and future directions"
    - "caching at the edge a green perspective for 5g networks"

Connected through hyperedge HE-6 (induced by source paper "content popularity prediction towards location aware mobile edge caching"):
  with members:
    - "cost effective cache deployment in mobile heterogeneous networks"
    - "dynamic mobile edge caching with location differentiation"
    - "context aware proactive content caching with service differentiation in wireless networks"
    - "wireless device to device caching networks basic principles and system performance"
    - "a learning based approach to caching in heterogenous small cell networks"
    - "wireless content caching for small cell and d2d networks"
    - "content centric sparse multicast beamforming for cache enabled cloud ran"
    - "learning based optimization of cache content in a small cell base station"

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.MM(Multimedia)