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: "the evolution of first person vision methods a survey"
  Abstract: The emergence of new wearable technologies, such as action cameras and smart glasses, has increased the interest of computer vision scientists in the first person perspective. Nowadays, this field is attracting attention and investments of companies aiming to develop commercial devices with first person vision (FPV) recording capabilities. Due to this interest, an increasing demand of methods to process these videos, possibly in real time, is expected. The current approaches present a particular combinations of different image features and quantitative methods to accomplish specific objectives like object detection, activity recognition, user-machine interaction, and so on. This paper summarizes the evolution of the state of the art in FPV video analysis between 1997 and 2014, highlighting, among others, the most commonly used features, methods, challenges, and opportunities within the field.

Connected through hyperedge HE-1 (induced by source paper "analysis of the hands in egocentric vision a survey"):
  with members:
    - "an effective and efficient method for detecting hands in egocentric videos for rehabilitation applications"
    - "recurrent u net for resource constrained segmentation"
    - "h o unified egocentric recognition of 3d hand object poses and interactions"
    - "domain specific priors and meta learning for low shot first person action recognition"
    - "egocentric hand track and object based human action recognition"
    - "egocentric video a new tool for capturing hand use of individuals with spinal cord injury at home"
    - "scaling egocentric vision the epic kitchens dataset"
    - "analysis of hand segmentation in the wild"

Connected through hyperedge HE-2 (induced by source paper "mutual context network for jointly estimating egocentric gaze and actions"):
  with members:
    - "visual dynamics stochastic future generation via layered cross convolutional networks"
    - "digging deeper into egocentric gaze prediction"
    - "attention is all we need nailing down object centric attention for egocentric activity recognition"
    - "on the role of event boundaries in egocentric activity recognition from photostreams"
    - "predicting gaze in egocentric video by learning task dependent attention transition"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "the kinetics human action video dataset"
    - "salgan visual saliency prediction with generative adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "a correlation based feature representation for first person activity recognition"):
  with members:
    - "convolutional long short term memory networks for recognizing first person interactions"
    - "learning latent sub events in activity videos using temporal attention filters"
    - "temporal segment networks towards good practices for deep action recognition"
    - "spatiotemporal residual networks for video action recognition"
    - "deep residual learning for image recognition"
    - "draw a recurrent neural network for image generation"
    - "action recognition with trajectory pooled deep convolutional descriptors"
    - "analyzing the performance of multilayer neural networks for object recognition"

Connected through hyperedge HE-4 (induced by source paper "digging deeper into egocentric gaze prediction"):
  with members:
    - "predicting gaze in egocentric video by learning task dependent attention transition"
    - "first person action object detection with egonet"
    - "detecting engagement in egocentric video"
    - "two stream convolutional networks for dynamic saliency prediction"
    - "vanishing point detection with convolutional neural networks"
    - "recurrent mixture density network for spatiotemporal visual attention"
    - "generating videos with scene dynamics"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "forecasting user attention during everyday mobile interactions using device integrated and wearable sensors"):
  with members:
    - "predicting gaze in egocentric video by learning task dependent attention transition"
    - "mpiigaze real world dataset and deep appearance based gaze estimation"
    - "going deeper into first person activity recognition"
    - "conditional random fields as recurrent neural networks"
    - "appearance based gaze estimation in the wild"
    - "places205 vggnet models for scene recognition"
    - "tabletgaze unconstrained appearance based gaze estimation in mobile tablets"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-6 (induced by source paper "predicting gaze in egocentric video by learning task dependent attention transition"):
  with members:
    - "sca cnn spatial and channel wise attention in convolutional networks for image captioning"
    - "shallow and deep convolutional networks for saliency prediction"
    - "convolutional two stream network fusion for video action recognition"
    - "recurrent attentional networks for saliency detection"
    - "top down visual saliency guided by captions"
    - "learning deep features for discriminative localization"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-7 (induced by source paper "multi modal egocentric activity recognition using audio visual features"):
  with members:
    - "boosted multiple kernel learning for first person activity recognition"
    - "convolutional long short term memory networks for recognizing first person interactions"
    - "compact cnn for indexing egocentric videos"

Connected through hyperedge HE-8 (induced by source paper "from face recognition to models of identity a bayesian approach to learning about unknown identities from unsupervised data"):
  with members:
    - "facenet a unified embedding for face recognition and clustering"
    - "objective criteria for the evaluation of clustering methods"

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)