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: "scene recognition with cnns objects scales and dataset bias"
  Abstract: Since scenes are composed in part of objects, accurate recognition of scenes requires knowledge about both scenes and objects. In this paper we address two related problems: 1) scale induced dataset bias in multi-scale convolutional neural network (CNN) architectures, and 2) how to combine effectively scene-centric and object-centric knowledge (i.e. Places and ImageNet) in CNNs. An earlier attempt, Hybrid-CNN[23], showed that incorporating ImageNet did not help much. Here we propose an alternative method taking the scale into account, resulting in significant recognition gains. By analyzing the response of ImageNet-CNNs and Places-CNNs at different scales we find that both operate in different scale ranges, so using the same network for all the scales induces dataset bias resulting in limited performance. Thus, adapting the feature extractor to each particular scale (i.e. scale-specific CNNs) is crucial to improve recognition, since the objects in the scenes have their specific range of scales. Experimental results show that the recognition accuracy highly depends on the scale, and that simple yet carefully chosen multi-scale combinations of ImageNet-CNNs and Places-CNNs, can push the stateof-the-art recognition accuracy in SUN397 up to 66.26% (and even 70.17% with deeper architectures, comparable to human performance).

Connected through hyperedge HE-1 (induced by source paper "indoor scene recognition in 3d"):
  with members:
    - "4d spatio temporal convnets minkowski convolutional neural networks"
    - "self supervised model adaptation for multimodal semantic segmentation"
    - "associatively segmenting instances and semantics in point clouds"
    - "3d instance segmentation via multi task metric learning"
    - "learning effective rgb d representations for scene recognition"
    - "mesh r cnn"
    - "dynamic graph cnn for learning on point clouds"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"

Connected through hyperedge HE-2 (induced by source paper "an analysis of pre training on object detection"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "rethinking imagenet pre training"
    - "large scale fine grained categorization and domain specific transfer learning"
    - "cornernet detecting objects as paired keypoints"
    - "do better imagenet models transfer better"
    - "taskonomy disentangling task transfer learning"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"

Connected through hyperedge HE-3 (induced by source paper "are we done with object recognition the icub robot s perspective"):
  with members:
    - "robotic tactile perception of object properties a review"
    - "learning hand eye coordination for robotic grasping with deep learning and large scale data collection"
    - "deep image retrieval learning global representations for image search"
    - "improved deep learning of object category using pose information"
    - "the curious robot learning visual representations via physical interactions"
    - "physically based rendering for indoor scene understanding using convolutional neural networks"
    - "a deep representation for depth images from synthetic data"
    - "learning to recognize objects by retaining other factors of variation"

Connected through hyperedge HE-4 (induced by source paper "fosnet an end to end trainable deep neural network for scene recognition"):
  with members:
    - "from volcano to toyshop adaptive discriminative region discovery for scene recognition"
    - "partial convolution based padding"
    - "hybrid cnn and dictionary based models for scene recognition and domain adaptation"
    - "a robust indoor scene recognition method based on sparse representation"
    - "learnable pooling with context gating for video classification"
    - "knowledge guided disambiguation for large scale scene classification with multi resolution cnns"
    - "squeeze and excitation networks"
    - "weakly supervised patchnets describing and aggregating local patches for scene recognition"

Connected through hyperedge HE-5 (induced by source paper "controlling biases and diversity in diverse image to image translation"):
  with members:
    - "exploiting unlabeled data in cnns by self supervised learning to rank"
    - "identifying and correcting label bias in machine learning"
    - "synthetic data generation for end to end thermal infrared tracking"
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "women also snowboard overcoming bias in captioning models"
    - "mix and match networks encoder decoder alignment for zero pair image translation"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "multimodal unsupervised image to image translation"

Connected through hyperedge HE-6 (induced by source paper "scene recognition with prototype agnostic scene layout"):
  with members:
    - "semantic fisher scores for task transfer using objects to classify scenes"
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "from volcano to toyshop adaptive discriminative region discovery for scene recognition"
    - "graph r cnn for scene graph generation"
    - "a deeper look at power normalizations"
    - "situation recognition with graph neural networks"
    - "network dissection quantifying interpretability of deep visual representations"
    - "hybrid cnn and dictionary based models for scene recognition and domain adaptation"

Connected through hyperedge HE-7 (induced by source paper "hierarchical approach to classify food scenes in egocentric photo streams"):
  with members:
    - "batch based activity recognition from egocentric photo streams revisited"
    - "macnet multi scale atrous convolution networks for food places classification in egocentric photo streams"
    - "r clustering for egocentric video segmentation"
    - "sr clustering semantic regularized clustering for egocentric photo streams segmentation"
    - "places an image database for deep scene understanding"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "object scene convolutional neural networks for event recognition in images"
    - "lsun construction of a large scale image dataset using deep learning with humans in the loop"

Connected through hyperedge HE-8 (induced by source paper "do better imagenet models transfer better"):
  with members:
    - "a closer look at few shot classification"
    - "object part attention model for fine grained image classification"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "adapted deep embeddings a synthesis of methods for k shot inductive transfer learning"
    - "taskonomy disentangling task transfer learning"
    - "autoaugment learning augmentation policies from data"
    - "exploring the limits of weakly supervised pretraining"
    - "three mechanisms of weight decay regularization"

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)