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: "big data caching for networking moving from cloud to edge"
  Abstract: In order to cope with the relentless data tsunami in 5G wireless networks, current approaches such as acquiring new spectrum, deploying more BSs, and increasing nodes in mobile packet core networks are becoming ineffective in terms of scalability, cost, and flexibility. In this regard, context- aware 5G networks with edge/cloud computing and exploitation of big data analytics can yield significant gains for mobile operators. In this article, proactive content caching in 5G wireless networks is investigated in which a big-data-enabled architecture is proposed. In this practical architecture, a vast amount of data is harnessed for content popularity estimation, and strategic contents are cached at BSs to achieve higher user satisfaction and backhaul offloading. To validate the proposed solution, we consider a real-world case study where several hours worth of mobile data traffic is collected from a major telecom operator in Turkey, and big-data-enabled analysis is carried out, leveraging tools from machine learning. Based on the available information and storage capacity, numerical studies show that several gains are achieved in terms of both user satisfaction and backhaul offloading. For example, in the case of 16 BSs with 30 percent of content ratings and 13 GB storage size (78 percent of total library size), proactive caching yields 100 percent user satisfaction and offloads 98 percent of the backhaul.

Connected through hyperedge HE-1 (induced by source paper "fog computing systems state of the art research issues and future trends"):
  with members:
    - "5g network slicing using sdn and nfv a survey of taxonomy architectures and future challenges"
    - "a roadmap toward the resilient internet of things for cyber physical systems"
    - "game theory for multi access edge computing survey use cases and future trends"
    - "a blockchain enabled trustless crowd intelligence ecosystem on mobile edge computing"
    - "machine learning for resource management in cellular and iot networks potentials current solutions and open challenges"
    - "all one needs to know about fog computing and related edge computing paradigms a complete survey"
    - "mitigating jamming attacks using energy harvesting"
    - "game theoretical modelling of network cybersecurity"

Connected through hyperedge HE-2 (induced by source paper "reinforcement learning based cooperative coded caching under dynamic popularities in ultra dense networks"):
  with members:
    - "spectrum sharing in vehicular networks based on multi agent reinforcement learning"
    - "deep learning based channel estimation for massive mimo with mixed resolution adcs"
    - "deep learning based wireless resource allocation with application to vehicular networks"
    - "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"
    - "multi agent learning for cooperative large scale caching networks"
    - "deep reinforcement learning based resource allocation for v2v communications"
    - "random caching based cooperative transmission in heterogeneous wireless networks"

Connected through hyperedge HE-3 (induced by source paper "decentralized coded caching without file splitting"):
  with members:
    - "the exact rate memory tradeoff for caching with uncoded prefetching"
    - "coded caching with linear subpacketization is possible using ruzsa szem eredi graphs"
    - "caching at the wireless edge design aspects challenges and future directions"
    - "wireless caching technical misconceptions and business barriers"
    - "an efficient coded multicasting scheme preserving the multiplicative caching gain"
    - "finite length analysis of caching aided coded multicasting"
    - "effect of number of users in multi level coded caching"
    - "coded caching for delay sensitive content"

Connected through hyperedge HE-4 (induced by source paper "slsgd secure and efficient distributed on device machine learning"):
  with members:
    - "analyzing federated learning through an adversarial lens"
    - "security and privacy issues in deep learning"
    - "federated learning strategies for improving communication efficiency"
    - "byzantine robust distributed learning towards optimal statistical rates"
    - "how to backdoor federated learning"
    - "mitigating sybils in federated learning poisoning"
    - "phocas dimensional byzantine resilient stochastic gradient descent"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-5 (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"
    - "understanding performance of edge content caching for mobile video streaming"
    - "wireless caching technical misconceptions and business barriers"
    - "energy efficiency of downlink networks with caching at base stations"

Connected through hyperedge HE-6 (induced by source paper "asynchronous federated optimization"):
  with members:
    - "towards federated learning at scale system design"
    - "federated learning strategies for improving communication efficiency"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "machine learning for internet of things data analysis a survey"
    - "asynchronous stochastic gradient descent with delay compensation"
    - "communication efficient learning of deep networks from decentralized data"
    - "federated optimization distributed optimization beyond the datacenter"
    - "mxnet a flexible and efficient machine learning library for heterogeneous distributed systems"

Connected through hyperedge HE-7 (induced by source paper "on the impact of satellite communications over mobile networks an experimental analysis"):
  with members:
    - "architectures and key technical challenges for 5g systems incorporating satellites"
    - "beam tracking for uav mounted satcom on the move with massive antenna array"
    - "lte based satellite communications in leo mega constellations"

Connected through hyperedge HE-8 (induced by source paper "emerging edge computing technologies for distributed iot systems"):
  with members:
    - "stochastic joint radio and computational resource management for multi user mobile edge computing systems"
    - "machine learning for internet of things data analysis a survey"

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.NI(Networking and Internet Architecture)