human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "unsupervised gaze prediction in egocentric videos by energy based surprise modeling"):
  Abstract: Egocentric perception has grown rapidly with the advent of immersive computing devices. Human gaze prediction is an important problem in analyzing egocentric videos and has largely been tackled through either saliency-based modeling or highly supervised learning. In this work, we tackle the problem of jointly predicting human gaze points and temporal segmentation of egocentric videos, in an unsupervised manner without using any training data. We introduce an unsupervised computational model that draws inspiration from cognitive psychology models of human attention and event perception. We use Grenander's pattern theory formalism to represent spatial-temporal features and model surprise as a mechanism to predict gaze fixation points and temporally segment egocentric videos. Extensive evaluation on two publicly available datasets - GTEA and GTEA+ datasets show that the proposed model is able to outperform all unsupervised baselines and some supervised gaze prediction baselines. Finally, we show that the model can also temporally segment egocentric videos with a performance comparable to more complex, fully supervised deep learning baselines.

Includes members:
  Member "predicting gaze in egocentric video by learning task dependent attention transition":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "ego topo environment affordances from egocentric video")
  Member "temporal convolutional networks a unified approach to action segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "v4d 4d convolutional neural networks for video level representation learning")
  Member "you only look once unified real time object detection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "object detection in optical remote sensing images a survey and a new benchmark")

Question: Please tell me which class the source paper 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)