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: "predicting gaze in egocentric video by learning task dependent attention transition"
  Abstract: We present a new computational model for gaze prediction in egocentric videos by exploring patterns in temporal shift of gaze fixations (attention transition) that are dependent on egocentric manipulation tasks. Our assumption is that the high-level context of how a task is completed in a certain way has a strong influence on attention transition and should be modeled for gaze prediction in natural dynamic scenes. Specifically, we propose a hybrid model based on deep neural networks which integrates task-dependent attention transition with bottom-up saliency prediction. In particular, the task-dependent attention transition is learned with a recurrent neural network to exploit the temporal context of gaze fixations, e.g. looking at a cup after moving gaze away from a grasped bottle. Experiments on public egocentric activity datasets show that our model significantly outperforms state-of-the-art gaze prediction methods and is able to learn meaningful transition of human attention.

Connected through hyperedge HE-1 (induced by source paper "ego topo environment affordances from egocentric video"):
  with members:
    - "action anticipation with rbf kernelized feature mapping rnn"
    - "scene memory transformer for embodied agents in long horizon tasks"
    - "videograph recognizing minutes long human activities in videos"
    - "anticipation and next action forecasting in video an end to end model with memory"
    - "generative hybrid representations for activity forecasting with no regret learning"
    - "what would you expect anticipating egocentric actions with rolling unrolling lstms and modality attention"
    - "long term feature banks for detailed video understanding"
    - "binge watching scaling affordance learning from sitcoms"

Connected through hyperedge HE-2 (induced by source paper "interaction graphs for object importance estimation in on road driving videos"):
  with members:
    - "goal oriented object importance estimation in on road driving videos"
    - "deeply supervised salient object detection with short connections"
    - "graph r cnn for scene graph generation"
    - "videos as space time region graphs"
    - "predicting driver attention in critical situations"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "the kinetics human action video dataset"
    - "amulet aggregating multi level convolutional features for salient object detection"

Connected through hyperedge HE-3 (induced by source paper "hand priming in object localization for assistive egocentric vision"):
  with members:
    - "digging deeper into egocentric gaze prediction"
    - "h o unified egocentric recognition of 3d hand object poses and interactions"
    - "scaling egocentric vision the epic kitchens dataset"
    - "vizwiz grand challenge answering visual questions from blind people"
    - "next active object prediction from egocentric videos"
    - "mask r cnn"
    - "fully convolutional instance aware semantic segmentation"
    - "unsupervised learning of important objects from first person videos"

Connected through hyperedge HE-4 (induced by source paper "magiceyes a large scale eye gaze estimation dataset for mixed reality"):
  with members:
    - "ritnet real time semantic segmentation of the eye for gaze tracking"
    - "appearance based 3d gaze estimation with personal calibration"
    - "unsupervised learning of depth and ego motion from video"
    - "pupilnet v2 0 convolutional neural networks for cpu based real time robust pupil detection"
    - "mpiigaze real world dataset and deep appearance based gaze estimation"
    - "eye tracking for everyone"
    - "learning from simulated and unsupervised images through adversarial training"
    - "labelled pupils in the wild a dataset for studying pupil detection in unconstrained environments"

Connected through hyperedge HE-5 (induced by source paper "unsupervised gaze prediction in egocentric videos by energy based surprise modeling"):
  with members:
    - "temporal convolutional networks a unified approach to action segmentation"
    - "you only look once unified real time object detection"

Connected through hyperedge HE-6 (induced by source paper "multitask learning to improve egocentric action recognition"):
  with members:
    - "mutual context network for jointly estimating egocentric gaze and actions"
    - "saliency tubes visual explanations for spatio temporal convolutions"
    - "egocentric hand track and object based human action recognition"
    - "what would you expect anticipating egocentric actions with rolling unrolling lstms and modality attention"
    - "long term feature banks for detailed video understanding"
    - "scaling egocentric vision the epic kitchens dataset"
    - "multi fiber networks for video recognition"
    - "yolov3 an incremental improvement"

Connected through hyperedge HE-7 (induced by source paper "a self validation network for object level human attention estimation"):
  with members:
    - "deeply supervised salient object detection with short connections"
    - "scaling egocentric vision the epic kitchens dataset"
    - "object level visual reasoning in videos"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "attention is all you need"
    - "the kinetics human action video dataset"
    - "instance level salient object segmentation"
    - "amulet aggregating multi level convolutional features for salient object detection"

Connected through hyperedge HE-8 (induced by source paper "manipulation skill assessment from videos with spatial attention network"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "mutual context network for jointly estimating egocentric gaze and actions"
    - "boosted attention leveraging human attention for image captioning"
    - "bam bottleneck attention module"
    - "learning to score the figure skating sports videos"
    - "tool detection and operative skill assessment in surgical videos using region based convolutional neural networks"
    - "cbam convolutional block attention module"
    - "video and accelerometer based motion analysis for automated surgical skills assessment"

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)