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: "enhanced alignment measure for binary foreground map evaluation"
  Abstract: The existing binary foreground map (FM) measures to address various types of errors in either pixel-wise or structural ways. These measures consider pixel-level match or image-level information independently, while cognitive vision studies have shown that human vision is highly sensitive to both global information and local details in scenes. In this paper, we take a detailed look at current binary FM evaluation measures and propose a novel and effective E-measure (Enhanced-alignment measure). Our measure combines local pixel values with the image-level mean value in one term, jointly capturing image-level statistics and local pixel matching information. We demonstrate the superiority of our measure over the available measures on 4 popular datasets via 5 meta-measures, including ranking models for applications, demoting generic, random Gaussian noise maps, ground-truth switch, as well as human judgments. We find large improvements in almost all the meta-measures. For instance, in terms of application ranking, we observe improvementrangingfrom9.08% to 19.65% compared with other popular measures.

Connected through hyperedge HE-1 (induced by source paper "weakly supervised salient object detection via scribble annotations"):
  with members:
    - "pyramid feature attention network for saliency detection"
    - "gated crf loss for weakly supervised semantic image segmentation"
    - "cascaded partial decoder for fast and accurate salient object detection"
    - "egnet edge guidance network for salient object detection"
    - "normalized cut loss for weakly supervised cnn segmentation"
    - "weakly supervised salient object detection using image labels"
    - "on regularized losses for weakly supervised cnn segmentation"
    - "revisiting dilated convolution a simple approach for weakly and semi supervised semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "a fixation based 360 benchmark dataset for salient object detection"):
  with members:
    - "global context aware progressive aggregation network for salient object detection"
    - "distortion adaptive salient object detection in 360 circ omnidirectional images"
    - "predicting head movement in panoramic video a deep reinforcement learning approach"
    - "rethinking rgb d salient object detection models datasets and large scale benchmarks"
    - "a simple pooling based design for real time salient object detection"
    - "cascaded partial decoder for fast and accurate salient object detection"
    - "egnet edge guidance network for salient object detection"
    - "bridge the gap between vqa and human behavior on omnidirectional video a large scale dataset and a deep learning model"

Connected through hyperedge HE-3 (induced by source paper "efficient and accurate arbitrary shaped text detection with pixel aggregation network"):
  with members:
    - "rethinking rgb d salient object detection models datasets and large scale benchmarks"
    - "selective kernel networks"
    - "shape robust text detection with progressive scale expansion network"
    - "rotation sensitive regression for oriented scene text detection"
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "arbitrary oriented scene text detection via rotation proposals"
    - "cu net coupled u nets"
    - "textsnake a flexible representation for detecting text of arbitrary shapes"

Connected through hyperedge HE-4 (induced by source paper "rethinking rgb d salient object detection models datasets and large scale benchmarks"):
  with members:
    - "super diffusion for salient object detection"
    - "adaptive fusion for rgb d salient object detection"
    - "salient object detection in the deep learning era an in depth survey"
    - "salient object detection with lossless feature reflection and weighted structural loss"
    - "multi source weak supervision for saliency detection"
    - "saliency integration an arbitrator model"
    - "richer and deeper supervision network for salient object detection"
    - "deeply supervised salient object detection with short connections"

Connected through hyperedge HE-5 (induced by source paper "salient instance segmentation via subitizing and clustering"):
  with members:
    - "salient object detection a survey"
    - "deeply supervised salient object detection with short connections"
    - "structure measure a new way to evaluate foreground maps"
    - "squeeze and excitation networks"
    - "instance level salient object segmentation"
    - "amulet aggregating multi level convolutional features for salient object detection"
    - "mask r cnn"
    - "deep contrast learning for salient object detection"

Connected through hyperedge HE-6 (induced by source paper "f3net fusion feedback and focus for salient object detection"):
  with members:
    - "salient object detection in the deep learning era an in depth survey"
    - "pyramid feature attention network for saliency detection"
    - "a simple pooling based design for real time salient object detection"
    - "cascaded partial decoder for fast and accurate salient object detection"
    - "deeply supervised salient object detection with short connections"
    - "reverse attention for salient object detection"
    - "structure measure a new way to evaluate foreground maps"
    - "squeeze and excitation networks"

Connected through hyperedge HE-7 (induced by source paper "scoot a perceptual metric for facial sketches"):
  with members:
    - "rethinking rgb d salient object detection models datasets and large scale benchmarks"
    - "nima neural image assessment"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "link the head to the beak zero shot learning from noisy text description at part precision"
    - "a multilayer based framework for online background subtraction with freely moving cameras"
    - "multi channel weighted nuclear norm minimization for real color image denoising"
    - "random sampling for fast face sketch synthesis"
    - "structure measure a new way to evaluate foreground maps"

Connected through hyperedge HE-8 (induced by source paper "salient objects in clutter bringing salient object detection to the foreground"):
  with members:
    - "deeply supervised salient object detection with short connections"
    - "learning uncertain convolutional features for accurate saliency detection"
    - "structure measure a new way to evaluate foreground maps"
    - "stc a simple to complex framework for weakly supervised semantic segmentation"
    - "instance level salient object segmentation"
    - "amulet aggregating multi level convolutional features for salient object detection"
    - "salient object detection a discriminative regional feature integration approach"
    - "deep saliency with encoded low level distance map and high level features"

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)