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: "label less learning for traffic control in an edge network"
  Abstract: With the development of intelligent applications (e.g., self-driving, real-time emotion recognition, etc), there are higher requirements for the cloud intelligence. However, cloud intelligence depends on the multi-modal data collected by user equipments (UEs). Due to the limited capacity of network bandwidth, offloading all data generated from the UEs to the remote cloud is impractical. Thus, in this article, we consider the challenging issue of achieving a certain level of cloud intelligence while reducing network traffic. In order to solve this problem, we design a traffic control algorithm based on label-less learning on the edge cloud, which is dubbed as LLTC. By the use of the limited computing and storage resources at edge cloud, LLTC evaluates the value of data, which will be offloaded. Specifically, we first give a statement of the problem and the system architecture. Then, we design the LLTC algorithm in detail. Finally, we set up the system testbed. Experimental results show that the proposed LLTC can guarantee the required cloud intelligence while minimizing the amount of data transmission.

Connected through hyperedge HE-1 (induced by source paper "data importance aware user scheduling for communication efficient edge machine learning"):
  with members:
    - "machine learning at the wireless edge distributed stochastic gradient descent over the air"
    - "optimizing pipelined computation and communication for latency constrained edge learning"
    - "accelerating dnn training in wireless federated edge learning system"
    - "edge intelligence paving the last mile of artificial intelligence with edge computing"
    - "towards federated learning at scale system design"
    - "semantic effectiveness filtering and control for post 5g wireless connectivity"
    - "mobile edge intelligence and computing for the internet of vehicles"
    - "active learning solution on distributed edge computing"

Connected through hyperedge HE-2 (induced by source paper "ai skin skin disease recognition based on self learning and wide data collection through a closed loop framework"):
  with members:
    - "dermatologist level dermoscopy skin cancer classification using different deep learning convolutional neural networks algorithms"
    - "what evidence does deep learning model use to classify skin lesions"
    - "wearable affective robot"
    - "convolutional neural networks for medical image analysis full training or fine tuning"
    - "very deep convolutional networks for large scale image recognition"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-3 (induced by source paper "a sustainable multi modal multi layer emotion aware service at the edge"):
  with members:
    - "a dynamic service migration mechanism in edge cognitive computing"
    - "wearable affective robot"
    - "energy costs for traffic offloading by cache enabled d2d communications"
    - "encoding source language with convolutional neural network for machine translation"
    - "efficient multi user computation offloading for mobile edge cloud computing"

Connected through hyperedge HE-4 (induced by source paper "ready player one uav clustering based multi task offloading for vehicular vr ar gaming"):
  with members:
    - "uav aided offloading for cellular hotspot"
    - "a dynamic service migration mechanism in edge cognitive computing"
    - "wearable affective robot"
    - "efficient deployment of multiple unmanned aerial vehicles for optimal wireless coverage"

Connected through hyperedge HE-5 (induced by source paper "cognitive information measurements a new perspective"):
  with members:
    - "cognitive lpwan towards intelligent wireless services in hybrid low power wide area networks"
    - "five disruptive technology directions for 5g"

Connected through hyperedge HE-6 (induced by source paper "limited gradient descent learning with noisy labels"):
  with members:
    - "cognitive information measurements a new perspective"
    - "deep learning for classical japanese literature"
    - "co sampling training robust networks for extremely noisy supervision"
    - "joint optimization framework for learning with noisy labels"
    - "generalized cross entropy loss for training deep neural networks with noisy labels"
    - "using trusted data to train deep networks on labels corrupted by severe noise"
    - "emnist an extension of mnist to handwritten letters"
    - "mixup beyond empirical risk minimization"

Connected through hyperedge HE-7 (induced by source paper "wireless data acquisition for edge learning data importance aware retransmission"):
  with members:
    - "machine learning at the wireless edge distributed stochastic gradient descent over the air"
    - "towards federated learning at scale system design"
    - "wireless network intelligence at the edge"
    - "federated learning strategies for improving communication efficiency"
    - "broadband analog aggregation for low latency federated edge learning extended version"
    - "federated learning via over the air computation"
    - "fast analog transmission for high mobility wireless data acquisition in edge learning"
    - "towards an intelligent edge wireless communication meets machine learning"

Connected through hyperedge HE-8 (induced by source paper "distributed learning for low latency machine type communication in a massive internet of things"):
  with members:
    - "cognitive lpwan towards intelligent wireless services in hybrid low power wide area networks"
    - "lte lte a random access for massive machine type communications in smart cities"
    - "learning how to communicate in the internet of things finite resources and heterogeneity"
    - "learning with finite memory for machine type communication"
    - "unmanned aerial vehicle with underlaid device to device communications performance and tradeoffs"
    - "what can wireless cellular technologies do about the upcoming smart metering traffic"
    - "reliable reporting for massive m2m communications with periodic resource pooling"
    - "fundamentals of throughput maximization with random arrivals for m2m communications"

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)