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: "social robots for people with developmental disabilities a user study on design features of a graphical user interface"
  Abstract: Social robots, also known as service or assistant robots, have been developed to improve the quality of human life in recent years. The design of socially capable and intelligent robots can vary, depending on the target user groups. In this work, we assess the effect of social robots' roles, functions, and communication approaches in the context of a social agent providing service or entertainment to users with developmental disabilities. In this paper, we describe an exploratory study of interface design for a social robot that assists people suffering from developmental disabilities. We developed series of prototypes and tested one in a user study that included three residents with various function levels. This entire study had been recorded for the following qualitative data analysis. Results show that each design factor played a different role in delivering information and in increasing engagement. We also note that some of the fundamental design principles that would work for ordinary users did not apply to our target user group. We conclude that social robots could benefit our target users, and acknowledge that these robots were not suitable for certain scenarios based on the feedback from our users.

Connected through hyperedge HE-1 (induced by source paper "hyperparameters optimization for deep learning based emotion prediction for human robot interaction"):
  with members:
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "i know how you feel emotion recognition with facial landmarks"
    - "real time convolutional neural networks for emotion and gender classification"
    - "convolutional neural networks for facial expression recognition"
    - "facial emotion detection using convolutional neural networks and representational autoencoder units"
    - "going deeper in facial expression recognition using deep neural networks"
    - "modeling spatial and temporal cues for multi label facial action unit detection"
    - "going deeper with convolutions"

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.RO(Robotics)