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: "real time eye gaze direction classification using convolutional neural network"
  Abstract: Estimation eye gaze direction is useful in various human-computer interaction tasks. Knowledge of gaze direction can give valuable information regarding users point of attention. Certain patterns of eye movements known as eye accessing cues are reported to be related to the cognitive processes in the human brain. We propose a real-time framework for the classification of eye gaze direction and estimation of eye accessing cues. In the first stage, the algorithm detects faces using a modified version of the Viola-Jones algorithm. A rough eye region is obtained using geometric relations and facial landmarks. The eye region obtained is used in the subsequent stage to classify the eye gaze direction. A convolutional neural network is employed in this work for the classification of eye gaze direction. The proposed algorithm was tested on Eye Chimera database and found to outperform state of the art methods. The computational complexity of the algorithm is very less in the testing phase. The algorithm achieved an average frame rate of 24 fps in the desktop environment.

Connected through hyperedge HE-1 (induced by source paper "are you really looking at me a feature extraction framework for estimating interpersonal eye gaze from conventional video"):
  with members:
    - "a review and analysis of eye gaze estimation systems algorithms and performance evaluation methods in consumer platforms"
    - "appearance based gaze estimation in the wild"
    - "pupil an open source platform for pervasive eye tracking and mobile gaze based interaction"

Connected through hyperedge HE-2 (induced by source paper "appearance based gaze estimation using dilated convolutions"):
  with members:
    - "using variable dwell time to accelerate gaze based web browsing with two step selection"
    - "light weight head pose invariant gaze tracking"
    - "batch renormalization towards reducing minibatch dependence in batch normalized models"
    - "dilated residual networks"
    - "mpiigaze real world dataset and deep appearance based gaze estimation"
    - "understanding convolution for semantic segmentation"
    - "eye tracking for everyone"
    - "learning from simulated and unsupervised images through adversarial training"

Connected through hyperedge HE-3 (induced by source paper "a review and analysis of eye gaze estimation systems algorithms and performance evaluation methods in consumer platforms"):
  with members:
    - "eye tracking for everyone"
    - "facevr real time facial reenactment and eye gaze control in virtual reality"
    - "owl and lizard patterns of head pose and eye pose in driver gaze classification"
    - "appearance based gaze estimation in the wild"

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)