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: "efficient eye typing with 9 direction gaze estimation"
  Abstract: Vision based text entry systems aim to help disabled people achieve text communication using eye movement. Most previous methods have employed an existing eye tracker to predict gaze direction and design an input method based upon that. However, these methods can result in eye tracking quality becoming easily affected by various factors and lengthy amounts of time for calibration. Our paper presents a novel efficient gaze based text input method, which has the advantage of low cost and robustness. Users can type in words by looking at an on-screen keyboard and blinking. Rather than estimate gaze angles directly to track eyes, we introduce a method that divides the human gaze into nine directions. This method can effectively improve the accuracy of making a selection by gaze and blinks. We build a Convolutional Neural Network (CNN) model for 9-direction gaze estimation. On the basis of the 9-direction gaze, we use a nine-key T9 input method which is widely used in candy bar phones. Bar phones were very popular in the world decades ago and have cultivated strong user habits and language models. To train a robust gaze estimator, we created a large-scale dataset with images of eyes sourced from 25 people. According to the results from our experiments, our CNN model is able to accurately estimate different people's gaze under various lighting conditions by different devices. In considering disable people's needs, we removed the complex calibration process. The input methods can run in screen mode and portable off-screen mode. Moreover, The datasets used in our experiments are made available to the community to allow further experimentation.

Connected through hyperedge HE-1 (induced by source paper "touchless typing using head movement based gestures"):
  with members:
    - "hierarchical deep feature learning for decoding imagined speech from eeg"
    - "fine grained head pose estimation without keypoints"
    - "sequence to sequence learning as beam search optimization"

Connected through hyperedge HE-2 (induced by source paper "a differential approach for gaze estimation"):
  with members:
    - "improving few shot user specific gaze adaptation via gaze redirection synthesis"
    - "from depth data to head pose estimation a siamese approach"
    - "learning to compare relation network for few shot learning"
    - "mpiigaze real world dataset and deep appearance based gaze estimation"
    - "gated siamese convolutional neural network architecture for human re identification"
    - "3d gaze estimation from 2d pupil positions on monocular head mounted eye trackers"
    - "eye tracking for everyone"
    - "learning from simulated and unsupervised images through adversarial training"

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.CV(Computer Vision and Pattern Recognition)