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: "mutual context network for jointly estimating egocentric gaze and actions"
  Abstract: In this work, we address two coupled tasks of gaze prediction and action recognition in egocentric videos by exploring their mutual context. Our assumption is that in the procedure of performing a manipulation task, what a person is doing determines where the person is looking at, and the gaze point reveals gaze and non-gaze regions which contain important and complementary information about the undergoing action. We propose a novel mutual context network (MCN) that jointly learns action-dependent gaze prediction and gaze-guided action recognition in an end-to-end manner. Experiments on public egocentric video datasets demonstrate that our MCN achieves state-of-the-art performance of both gaze prediction and action recognition.

Connected through hyperedge HE-1 (induced by source paper "multitask learning to improve egocentric action recognition"):
  with members:
    - "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"
    - "taskonomy disentangling task transfer learning"

Connected through hyperedge HE-2 (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"
    - "boosted attention leveraging human attention for image captioning"
    - "bam bottleneck attention module"
    - "learning to score the figure skating sports videos"
    - "predicting gaze in egocentric video by learning task dependent attention transition"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "understanding teacher gaze patterns for robot learning"):
  with members:
    - "digging deeper into egocentric gaze prediction"
    - "a review of robot learning for manipulation challenges representations and algorithms"
    - "risk aware active inverse reinforcement learning"
    - "one shot learning of multi step tasks from observation via activity localization in auxiliary video"
    - "connecting gaze scene and attention generalized attention estimation via joint modeling of gaze and scene saliency"
    - "compact generalized non local network"
    - "non local neural networks"

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)