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: "exploring the bounds of the utility of context for object detection"
  Abstract: The recurring context in which objects appear holds valuable information that can be employed to predict their existence. This intuitive observation indeed led many researchers to endow appearance-based detectors with explicit reasoning about context. The underlying thesis suggests that stronger contextual relations would facilitate greater improvements in detection capacity. In practice, however, the observed improvement in many cases is modest at best, and often only marginal. In this work we seek to improve our understanding of this phenomenon, in part by pursuing an opposite approach. Instead of attempting to improve detection scores by employing context, we treat the utility of context as an optimization problem: to what extent can detection scores be improved by considering context or any other kind of additional information? With this approach we explore the bounds on improvement by using contextual relations between objects and provide a tool for identifying the most helpful ones. We show that simple co-occurrence relations can often provide large gains, while in other cases a significant improvement is simply impossible or impractical with either co-occurrence or more precise spatial relations. To better understand these results we then analyze the ability of context to handle different types of false detections, revealing that tested contextual information cannot ameliorate localization errors, severely limiting its gains. These and additional insights further our understanding on where and why utilization of context for object detection succeeds and fails.

Connected through hyperedge HE-1 (induced by source paper "don t judge an object by its context learning to overcome contextual bias"):
  with members:
    - "does object recognition work for everyone"
    - "mitigating gender bias in natural language processing literature review"
    - "repair removing representation bias by dataset resampling"
    - "class balanced loss based on effective number of samples"
    - "laso label set operations networks for multi label few shot learning"
    - "turning a blind eye explicit removal of biases and variation from deep neural network embeddings"
    - "women also snowboard overcoming bias in captioning models"
    - "learning not to learn training deep neural networks with biased data"

Connected through hyperedge HE-2 (induced by source paper "empirical upper bound in object detection and more"):
  with members:
    - "an analysis of pre training on object detection"
    - "libra r cnn towards balanced learning for object detection"
    - "generalized intersection over union a metric and a loss for bounding box regression"
    - "efficientdet scalable and efficient object detection"
    - "an empirical study of spatial attention mechanisms in deep networks"
    - "contextual relabelling of detected objects"
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "mask scoring r cnn"

Connected through hyperedge HE-3 (induced by source paper "adversarial patches exploiting contextual reasoning in object detection"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "one pixel attack for fooling deep neural networks"
    - "attacking optical flow"
    - "adversarial examples that fool both computer vision and time limited humans"
    - "not using the car to see the sidewalk quantifying and controlling the effects of context in classification and segmentation"
    - "lavan localized and visible adversarial noise"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "adversarial examples are not easily detected bypassing ten detection methods"

Connected through hyperedge HE-4 (induced by source paper "seeing without looking contextual rescoring of object detections for ap maximization"):
  with members:
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "structure inference net object detection using scene level context and instance level relationships"
    - "attention is all you need"
    - "inner scene similarities as a contextual cue for object detection"
    - "cascade r cnn delving into high quality object detection"
    - "learning non maximum suppression"
    - "spatial memory for context reasoning in object detection"
    - "accurate single stage detector using recurrent rolling convolution"

Connected through hyperedge HE-5 (induced by source paper "on the importance of visual context for data augmentation in scene understanding"):
  with members:
    - "rendering synthetic objects into legacy photographs"
    - "weakly supervised instance segmentation using class peak response"
    - "semantic foggy scene understanding with synthetic data"
    - "modeling visual context is key to augmenting object detection datasets"
    - "synthetic data augmentation using gan for improved liver lesion classification"
    - "improved part segmentation performance by optimising realism of synthetic images using cycle generative adversarial networks"
    - "random erasing data augmentation"
    - "focal loss for dense object detection"

Connected through hyperedge HE-6 (induced by source paper "modeling visual context is key to augmenting object detection datasets"):
  with members:
    - "rendering synthetic objects into legacy photographs"
    - "synthetic data augmentation using gan for improved liver lesion classification"
    - "random erasing data augmentation"
    - "focal loss for dense object detection"
    - "blitznet a real time deep network for scene understanding"
    - "cut paste and learn surprisingly easy synthesis for instance detection"
    - "dssd deconvolutional single shot detector"
    - "synthesizing training data for object detection in indoor scenes"

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)