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 "deep learning based caching for self driving car in multi access edge computing"):
  Abstract: Once self-driving car becomes a reality and passengers are no longer worry about it, they will need to find new ways of entertainment. However, retrieving entertainment contents at the Data Center (DC) can hinder content delivery service due to high delay of car-to-DC communication. To address these challenges, we propose a deep learning based caching for self-driving car, by using Deep Learning approaches deployed on the Multi-access Edge Computing (MEC) structure. First, at DC, Multi-Layer Perceptron (MLP) is used to predict the probabilities of contents to be requested in specific areas. To reduce the car-DC delay, MLP outputs are logged into MEC servers attached to roadside units. Second, in order to cache entertainment contents stylized for car passengers' features such as age and gender, Convolutional Neural Network (CNN) is used to predict age and gender of passengers. Third, each car requests MLP output from MEC server and compares its CNN and MLP outputs by using k-means and binary classification. Through this, the self-driving car can identify the contents need to be downloaded from the MEC server and cached. Finally, we formulate deep learning based caching in the self-driving car that enhances entertainment services as an optimization problem whose goal is to minimize content downloading delay. To solve the formulated problem, a Block Successive Majorization-Minimization (BS-MM) technique is applied. The simulation results show that the accuracy of our prediction for the contents need to be cached in the areas of the self-driving car is achieved at 98.04% and our approach can minimize delay.

Includes members:
  Member "the history began from alexnet a comprehensive survey on deep learning approaches":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "when deep learning meets data alignment a review on deep registration networks drns")
  Member "joint communication computation caching and control in big data multi access edge computing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multi agent meta reinforcement learning for self powered and sustainable edge computing systems")
  Member "network slicing for service oriented networks under resource constraints":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "coexistence mechanism between embb and urllc in 5g wireless networks")
  Member "neutm a neural network based framework for traffic matrix prediction in sdn":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "cellular traffic prediction and classification a comparative evaluation of lstm and arima")
  Member "dager deep age gender and emotion recognition using convolutional neural network":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "exposing and correcting the gender bias in image captioning datasets and models")
  Member "optimal geographic caching in cellular networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "online convex optimization for caching networks")
  Member "very deep convolutional networks for large scale image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "discrimination aware network pruning for deep model compression")

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)