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 "predicting the category and attributes of visual search targets using deep gaze pooling"):
  Abstract: Predicting the target of visual search from eye fixation (gaze) data is a challenging problem with many applications in human-computer interaction. In contrast to previous work that has focused on individual instances as a search target, we propose the first approach to predict categories and attributes of search targets based on gaze data. However, state of the art models for categorical recognition, in general, require large amounts of training data, which is prohibitive for gaze data. To address this challenge, we propose a novel Gaze Pooling Layer that integrates gaze information into CNN-based architectures as an attention mechanism - incorporating both spatial and temporal aspects of human gaze behavior. We show that our approach is effective even when the gaze pooling layer is added to an already trained CNN, thus eliminating the need for expensive joint data collection of visual and gaze data. We propose an experimental setup and data set and demonstrate the effectiveness of our method for search target prediction based on gaze behavior. We further study how to integrate temporal and spatial gaze information most effectively, and indicate directions for future research in the gaze-based prediction of mental states.

Includes members:
  Member "seeing with humans gaze assisted neural image captioning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the fluidity of concept representations in human brain signals")
  Member "gaze embeddings for zero shot image classification":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "attention flow end to end joint attention estimation")
  Member "whittlesearch interactive image search with relative attribute feedback":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "dressing for diverse body shapes")
  Member "gazedpm early integration of gaze information in deformable part models":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a self validation network for object level human attention estimation")
  Member "learning deep features for discriminative localization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "ha ccn hierarchical attention based crowd counting network")
  Member "prediction of search targets from fixations in open world settings":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a self validation network for object level human attention estimation")
  Member "multiple instance reinforcement learning for efficient weakly supervised detection in images":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "tree structured reinforcement learning for sequential object localization")
  Member "the secrets of salient object segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "highly efficient salient object detection with 100k parameters")

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)