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: "expressionbot an emotive lifelike robotic face for face to face communication"
  Abstract: This article proposes an emotive lifelike robotic face, called ExpressionBot, that is designed to support verbal and non-verbal communication between the robot and humans, with the goal of closely modeling the dynamics of natural face-to-face communication. The proposed robotic head consists of two major components: 1) a hardware component that contains a small projector, a fish-eye lens, a custom-designed mask and a neck system with 3 degrees of freedom; 2) a facial animation system, projected onto the robotic mask, that is capable of presenting facial expressions, realistic eye movement, and accurate visual speech. We present three studies that compare Human-Robot Interaction with Human-Computer Interaction with a screen-based model of the avatar. The studies indicate that the robotic face is well accepted by users, with some advantages in recognition of facial expression and mutual eye gaze contact.

Connected through hyperedge HE-1 (induced by source paper "going deeper in facial expression recognition using deep neural networks"):
  with members:
    - "deepid3 face recognition with very deep neural networks"
    - "going deeper with convolutions"
    - "imagenet large scale visual recognition challenge"
    - "caffe convolutional architecture for fast feature embedding"
    - "deeppose human pose estimation via deep neural networks"
    - "ebear an expressive bear like robot"
    - "provable bounds for learning some deep representations"
    - "bidirectional warping of active appearance model"

Connected through hyperedge HE-2 (induced by source paper "facial expression recognition from world wild web"):
  with members:
    - "going deeper in facial expression recognition using deep neural networks"
    - "going deeper with convolutions"
    - "training convolutional networks with noisy labels"
    - "deeppose human pose estimation via deep neural networks"
    - "ebear an expressive bear like robot"
    - "bidirectional warping of active appearance model"
    - "deep learning using linear support vector machines"

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)