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 "pa cache learning based popularity aware content caching in edge networks"):
  Abstract: With the aggressive growth of smart environments, a large amount of data are generated by edge devices. As a result, content delivery has been quickly pushed to network edges. Compared with classical content delivery networks, edge caches with smaller size usually suffer from more bursty requests, which makes conventional caching algorithms perform poorly in edge networks. This paper aims to propose an effective caching decision policy called PA-Cache that uses evolving deep learning to adaptively learn time-varying content popularity to decide which content to evict when the cache is full. Unlike prior learning-based approaches that either use a small set of features for decision making or require the entire training dataset to be available for learning a fine-tuned but might outdated prediction model, PA-Cache weights a large set of critical features to train the neural network in an evolving manner so as to meet the edge requests with fluctuations and bursts. We demonstrate the effectiveness of PA-Cache through extensive experiments with real-world data traces from a large commercial video-on-demand service provider. The evaluation shows that PA-Cache improves the hit rate in comparison with state-of-the-art methods at a lower computational cost.

Includes members:
  Member "deep reinforcement learning for adaptive caching in hierarchical content delivery networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "cross layer scheduling and beamforming in smart grid powered cellular networks with heterogeneous energy coordination")
  Member "online deep learning learning deep neural networks on the fly":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "qactor on line active learning for noisy labeled stream data")
  Member "what when and where to cache a unified optimization approach":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "joint data compression and caching approaching optimality with guarantees")
  Member "optimizing timer based policies for general cache networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "network cache design under stationary requests exact analysis and poisson approximation")
  Member "understanding performance of edge content caching for mobile video streaming":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "caching at base stations with heterogeneous user demands and spatial locality")
  Member "caching at the wireless edge design aspects challenges and future directions":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "enhancing physical layer security of random caching in large scale multi antenna heterogeneous wireless networks")
  Member "fractalnet ultra deep neural networks without residuals":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "bats binary architecture search")
  Member "mxnet a flexible and efficient machine learning library for heterogeneous distributed systems":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "attention guided chained context aggregation for semantic segmentation")

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)