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: "wearable affective robot"
  Abstract: With the development of the artificial intelligence (AI), the AI applications have influenced and changed people's daily life greatly. Here, a wearable affective robot that integrates the affective robot, social robot, brain wearable, and wearable 2.0 is proposed for the first time. The proposed wearable affective robot is intended for a wide population, and we believe that it can improve the human health on the spirit level, meeting the fashion requirements at the same time. In this paper, the architecture and design of an innovative wearable affective robot, which is dubbed as Fitbot, are introduced in terms of hardware and algorithm's perspectives. In addition, the important functional component of the robot-brain wearable device is introduced from the aspect of the hardware design, EEG data acquisition and analysis, user behavior perception, and algorithm deployment, etc. Then, the EEG based cognition of user's behavior is realized. Through the continuous acquisition of the in-depth, in-breadth data, the Fitbot we present can gradually enrich user's life modeling and enable the wearable robot to recognize user's intention and further understand the behavioral motivation behind the user's emotion. The learning algorithm for the life modeling embedded in Fitbot can achieve better user's experience of affective social interaction. Finally, the application service scenarios and some challenging issues of a wearable affective robot are discussed.

Connected through hyperedge HE-1 (induced by source paper "ai skin skin disease recognition based on self learning and wide data collection through a closed loop framework"):
  with members:
    - "label less learning for traffic control in an edge network"
    - "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"
    - "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-2 (induced by source paper "a sustainable multi modal multi layer emotion aware service at the edge"):
  with members:
    - "label less learning for traffic control in an edge network"
    - "a dynamic service migration mechanism in edge cognitive computing"
    - "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-3 (induced by source paper "ready player one uav clustering based multi task offloading for vehicular vr ar gaming"):
  with members:
    - "label less learning for traffic control in an edge network"
    - "uav aided offloading for cellular hotspot"
    - "a dynamic service migration mechanism in edge cognitive computing"
    - "efficient deployment of multiple unmanned aerial vehicles for optimal wireless coverage"

Connected through hyperedge HE-4 (induced by source paper "cascade lstm based visual inertial navigation for magnetic levitation haptic interaction"):
  with members:
    - "vins mono a robust and versatile monocular visual inertial state estimator"

Connected through hyperedge HE-5 (induced by source paper "label less learning for traffic control in an edge network"):
  with members:
    - "mobile edge computing a survey on architecture and computation offloading"

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.HC(Human-Computer Interaction)