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: "knowledge guided disambiguation for large scale scene classification with multi resolution cnns"
  Abstract: Convolutional neural networks (CNNs) have made remarkable progress on scene recognition, partially due to these recent large-scale scene datasets, such as the Places and Places2. Scene categories are often defined by multi-level information, including local objects, global layout, and background environment, thus leading to large intra-class variations. In addition, with the increasing number of scene categories, label ambiguity has become another crucial issue in large-scale classification. This paper focuses on large-scale scene recognition and makes two major contributions to tackle these issues. First, we propose a multi-resolution CNN architecture that captures visual content and structure at multiple levels. The multi-resolution CNNs are composed of coarse resolution CNNs and fine resolution CNNs, which are complementary to each other. Second, we design two knowledge guided disambiguation techniques to deal with the problem of label ambiguity: 1) we exploit the knowledge from the confusion matrix computed on validation data to merge ambiguous classes into a super category and 2) we utilize the knowledge of extra networks to produce a soft label for each image. Then, the super categories or soft labels are employed to guide CNN training on the Places2. We conduct extensive experiments on three large-scale image datasets (ImageNet, Places, and Places2), demonstrating the effectiveness of our approach. Furthermore, our method takes part in two major scene recognition challenges, and achieves the second place at the Places2 challenge in ILSVRC 2015, and the first place at the LSUN challenge in CVPR 2016. Finally, we directly test the learned representations on other scene benchmarks, and obtain the new state-of-the-art results on the MIT Indoor67 (86.7%) and SUN397 (72.0%). We release the code and models at  https://github.com/wanglimin/MRCNN-Scene-Recognition .

Connected through hyperedge HE-1 (induced by source paper "knowledge integration networks for action recognition"):
  with members:
    - "geometric pose affordance 3d human pose with scene constraints"
    - "eco efficient convolutional network for online video understanding"
    - "temporal bilinear networks for video action recognition"
    - "stnet local and global spatial temporal modeling for action recognition"
    - "actor action semantic segmentation with region masks"
    - "videos as space time region graphs"
    - "slowfast networks for video recognition"
    - "quo vadis action recognition a new model and the kinetics dataset"

Connected through hyperedge HE-2 (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"
    - "squeeze and excitation networks"
    - "weakly supervised patchnets describing and aggregating local patches for scene recognition"
    - "accurate large minibatch sgd training imagenet in 1 hour"

Connected through hyperedge HE-3 (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-4 (induced by source paper "dynamic traffic scene classification with space time coherence"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "the apolloscape open dataset for autonomous driving and its application"
    - "don t look back robustifying place categorization for viewpoint and condition invariant place recognition"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "toward driving scene understanding a dataset for learning driver behavior and causal reasoning"
    - "rethinking the faster r cnn architecture for temporal action localization"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "traffic scene recognition based on deep cnn and vlad spatial pyramids"

Connected through hyperedge HE-5 (induced by source paper "translate to recognize networks for rgb d scene recognition"):
  with members:
    - "learning effective rgb d representations for scene recognition"
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "scene flow to action map a new representation for rgb d based action recognition with convolutional neural networks"
    - "perceptual losses for real time style transfer and super resolution"
    - "generative image modeling using style and structure adversarial networks"
    - "deeper depth prediction with fully convolutional residual networks"
    - "image to image translation with conditional adversarial networks"
    - "colorful image colorization"

Connected through hyperedge HE-6 (induced by source paper "curriculumnet weakly supervised learning from large scale web images"):
  with members:
    - "deep learning is robust to massive label noise"
    - "focal loss for dense object detection"
    - "cleannet transfer learning for scalable image classifier training with label noise"
    - "foodnet recognizing foods using ensemble of deep networks"
    - "squeeze and excitation networks"
    - "webvision challenge visual learning and understanding with web data"
    - "webvision database visual learning and understanding from web data"
    - "learning from noisy large scale datasets with minimal supervision"

Connected through hyperedge HE-7 (induced by source paper "from volcano to toyshop adaptive discriminative region discovery for scene recognition"):
  with members:
    - "fine grained discriminative localization via saliency guided faster r cnn"
    - "hybrid cnn and dictionary based models for scene recognition and domain adaptation"
    - "scene graph generation by iterative message passing"
    - "weakly supervised patchnets describing and aggregating local patches for scene recognition"
    - "object level context modeling for scene classification with context cnn"
    - "locally supervised deep hybrid model for scene recognition"
    - "scene recognition with cnns objects scales and dataset bias"
    - "harvesting discriminative meta objects with deep cnn features for scene classification"

Connected through hyperedge HE-8 (induced by source paper "locally supervised deep hybrid model for scene recognition"):
  with members:
    - "hybrid cnn and dictionary based models for scene recognition and domain adaptation"
    - "weakly supervised patchnets describing and aggregating local patches for scene recognition"
    - "text attentional convolutional neural network for scene text detection"
    - "learning contextual dependence with convolutional hierarchical recurrent neural networks"
    - "learning deep representation for face alignment with auxiliary attributes"
    - "detecting text in natural image with connectionist text proposal network"
    - "local color contrastive descriptor for image classification"
    - "reading scene text in deep convolutional sequences"

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)