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: "improving small object proposals for company logo detection"
  Abstract: Many modern approaches for object detection are two-staged pipelines. The first stage identifies regions of interest which are then classified in the second stage. Faster R-CNN is such an approach for object detection which combines both stages into a single pipeline. In this paper we apply Faster R-CNN to the task of company logo detection. Motivated by its weak performance on small object instances, we examine in detail both the proposal and the classification stage with respect to a wide range of object sizes. We investigate the influence of feature map resolution on the performance of those stages.   Based on theoretical considerations, we introduce an improved scheme for generating anchor proposals and propose a modification to Faster R-CNN which leverages higher-resolution feature maps for small objects. We evaluate our approach on the FlickrLogos dataset improving the RPN performance from 0.52 to 0.71 (MABO) and the detection performance from 0.52 to $0.67$ (mAP).

Connected through hyperedge HE-1 (induced by source paper "assessment of faster r cnn in man machine collaborative search"):
  with members:
    - "adversarial attacks against medical deep learning systems"
    - "adversarial examples that fool both computer vision and time limited humans"
    - "attention allocation aid for visual search"
    - "evaluating visual conversational agents via cooperative human ai games"
    - "chexnet radiologist level pneumonia detection on chest x rays with deep learning"
    - "machine teaching a new paradigm for building machine learning systems"
    - "a survey on deep learning in medical image analysis"
    - "comparing deep neural networks against humans object recognition when the signal gets weaker"

Connected through hyperedge HE-2 (induced by source paper "augmentation for small object detection"):
  with members:
    - "feature fused ssd fast detection for small objects"
    - "a systematic study of the class imbalance problem in convolutional neural networks"
    - "dssd deconvolutional single shot detector"
    - "perceptual generative adversarial networks for small object detection"
    - "the devil is in the decoder classification regression and gans"
    - "semantic instance segmentation via deep metric learning"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "mask r cnn"

Connected through hyperedge HE-3 (induced by source paper "simultaneous x y pixel estimation and feature extraction for multiple small objects in a scene a description of the alien network"):
  with members:
    - "drone based object counting by spatially regularized regional proposal network"
    - "a large contextual dataset for classification detection and counting of cars with deep learning"
    - "yolo9000 better faster stronger"
    - "you only look once unified real time object detection"
    - "fast r cnn"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "overfeat integrated recognition localization and detection using convolutional networks"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

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)