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 "hand priming in object localization for assistive egocentric vision"):
  Abstract: Egocentric vision holds great promises for increasing access to visual information and improving the quality of life for people with visual impairments, with object recognition being one of the daily challenges for this population. While we strive to improve recognition performance, it remains difficult to identify which object is of interest to the user; the object may not even be included in the frame due to challenges in camera aiming without visual feedback. Also, gaze information, commonly used to infer the area of interest in egocentric vision, is often not dependable. However, blind users often tend to include their hand either interacting with the object that they wish to recognize or simply placing it in proximity for better camera aiming. We propose localization models that leverage the presence of the hand as the contextual information for priming the center area of the object of interest. In our approach, hand segmentation is fed to either the entire localization network or its last convolutional layers. Using egocentric datasets from sighted and blind individuals, we show that the hand-priming achieves higher precision than other approaches, such as fine-tuning, multi-class, and multi-task learning, which also encode hand-object interactions in localization.

Includes members:
  Member "digging deeper into egocentric gaze prediction":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "mutual context network for jointly estimating egocentric gaze and actions")
  Member "h o unified egocentric recognition of 3d hand object poses and interactions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "yoloff you only learn offsets for robust 6dof object pose estimation")
  Member "scaling egocentric vision the epic kitchens dataset":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning to learn words from visual scenes")
  Member "vizwiz grand challenge answering visual questions from blind people":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "in defense of grid features for visual question answering")
  Member "predicting gaze in egocentric video by learning task dependent attention transition":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "ego topo environment affordances from egocentric video")
  Member "next active object prediction from egocentric videos":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "ego topo environment affordances from egocentric video")
  Member "mask r cnn":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "relatext exploiting visual relationships for arbitrary shaped scene text detection with graph convolutional networks")
  Member "fully convolutional instance aware semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "deep affinity net instance segmentation via affinity")

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)