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 "gaze360 physically unconstrained gaze estimation in the wild"):
  Abstract: Understanding where people are looking is an informative social cue. In this work, we present Gaze360, a large-scale gaze-tracking dataset and method for robust 3D gaze estimation in unconstrained images. Our dataset consists of 238 subjects in indoor and outdoor environments with labelled 3D gaze across a wide range of head poses and distances. It is the largest publicly available dataset of its kind by both subject and variety, made possible by a simple and efficient collection method. Our proposed 3D gaze model extends existing models to include temporal information and to directly output an estimate of gaze uncertainty. We demonstrate the benefits of our model via an ablation study, and show its generalization performance via a cross-dataset evaluation against other recent gaze benchmark datasets. We furthermore propose a simple self-supervised approach to improve cross-dataset domain adaptation. Finally, we demonstrate an application of our model for estimating customer attention in a supermarket setting. Our dataset and models are available at this http URL .

Includes members:
  Member "multiposenet fast multi person pose estimation using pose residual network":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "augmented parallel pyramid net for attention guided pose estimation")
  Member "densepose dense human pose estimation in the wild":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "neural human video rendering joint learning of dynamic textures and rendering to video translation")
  Member "adversarial discriminative domain adaptation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "unsupervised multi class domain adaptation theory algorithms and practice")
  Member "temporal relational reasoning in videos":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "era a dataset and deep learning benchmark for event recognition in aerial videos")
  Member "convolutional pose machines":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "posenet3d unsupervised 3d human shape and pose estimation")
  Member "rmpe regional multi person pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "hierarchical human parsing with typed part relation reasoning")
  Member "eye tracking for everyone":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "geddnet a network for gaze estimation with dilation and decomposition")
  Member "seeing with humans gaze assisted neural image captioning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "the fluidity of concept representations in human brain signals")

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)