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: "bayesian identification of fixations saccades and smooth pursuits"
  Abstract: Smooth pursuit eye movements provide meaningful insights and information on subject's behavior and health and may, in particular situations, disturb the performance of typical fixation/saccade classification algorithms. Thus, an automatic and efficient algorithm to identify these eye movements is paramount for eye-tracking research involving dynamic stimuli. In this paper, we propose the Bayesian Decision Theory Identification (I-BDT) algorithm, a novel algorithm for ternary classification of eye movements that is able to reliably separate fixations, saccades, and smooth pursuits in an online fashion, even for low-resolution eye trackers. The proposed algorithm is evaluated on four datasets with distinct mixtures of eye movements, including fixations, saccades, as well as straight and circular smooth pursuits; data was collected with a sample rate of 30 Hz from six subjects, totaling 24 evaluation datasets. The algorithm exhibits high and consistent performance across all datasets and movements relative to a manual annotation by a domain expert (recall: \mu = 91.42%, \sigma = 9.52%; precision: \mu = 95.60%, \sigma = 5.29%; specificity \mu = 95.41%, \sigma = 7.02%) and displays a significant improvement when compared to I-VDT, an state-of-the-art algorithm (recall: \mu = 87.67%, \sigma = 14.73%; precision: \mu = 89.57%, \sigma = 8.05%; specificity \mu = 92.10%, \sigma = 11.21%). For algorithm implementation and annotated datasets, please contact the first author.

Connected through hyperedge HE-1 (induced by source paper "gaze in wild a dataset for studying eye and head coordination in everyday activities"):
  with members:
    - "generalised dice overlap as a deep learning loss function for highly unbalanced segmentations"
    - "end to end eye movement detection using convolutional neural networks"
    - "adam a method for stochastic optimization"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"
    - "pupil an open source platform for pervasive eye tracking and mobile gaze based interaction"

Connected through hyperedge HE-2 (induced by source paper "what are you looking at detecting human intention in gaze based human robot interaction"):
  with members:
    - "free view 3d gaze guided assistive robotic system for activities of daily living"
    - "yolo9000 better faster stronger"
    - "deepfix a fully convolutional neural network for predicting human eye fixations"
    - "deep gaze i boosting saliency prediction with feature maps trained on imagenet"
    - "pupil an open source platform for pervasive eye tracking and mobile gaze based interaction"

Connected through hyperedge HE-3 (induced by source paper "360 degree video gaze behaviour a ground truth data set and a classification algorithm for eye movements"):
  with members:
    - "cube padding for weakly supervised saliency prediction in 360 deg videos"
    - "learning to compare image patches via convolutional neural networks"

Connected through hyperedge HE-4 (induced by source paper "pure robust pupil detection for real time pervasive eye tracking"):
  with members:
    - "a novel performance evaluation methodology for single target trackers"
    - "visual object tracking performance measures revisited"
    - "labelled pupils in the wild a dataset for studying pupil detection in unconstrained environments"
    - "pupilnet convolutional neural networks for robust pupil detection"
    - "else ellipse selection for robust pupil detection in real world environments"
    - "cudnn efficient primitives for deep learning"

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)