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: "computational baby learning"
  Abstract: Intuitive observations show that a baby may inherently possess the capability of recognizing a new visual concept (e.g., chair, dog) by learning from only very few positive instances taught by parent(s) or others, and this recognition capability can be gradually further improved by exploring and/or interacting with the real instances in the physical world. Inspired by these observations, we propose a computational model for slightly-supervised object detection, based on prior knowledge modelling, exemplar learning and learning with video contexts. The prior knowledge is modeled with a pre-trained Convolutional Neural Network (CNN). When very few instances of a new concept are given, an initial concept detector is built by exemplar learning over the deep features from the pre-trained CNN. Simulating the baby's interaction with physical world, the well-designed tracking solution is then used to discover more diverse instances from the massive online unlabeled videos. Once a positive instance is detected/identified with high score in each video, more variable instances possibly from different view-angles and/or different distances are tracked and accumulated. Then the concept detector can be fine-tuned based on these new instances. This process can be repeated again and again till we obtain a very mature concept detector. Extensive experiments on Pascal VOC-07/10/12 object detection datasets well demonstrate the effectiveness of our framework. It can beat the state-of-the-art full-training based performances by learning from very few samples for each object category, along with about 20,000 unlabeled videos.

Connected through hyperedge HE-1 (induced by source paper "self paced deep learning for weakly supervised object detection"):
  with members:
    - "bridging saliency detection to weakly supervised object detection based on self paced curriculum learning"
    - "stc a simple to complex framework for weakly supervised semantic segmentation"
    - "mask r cnn"
    - "training region based object detectors with online hard example mining"
    - "weakly supervised object localization using size estimates"
    - "contextlocnet context aware deep network models for weakly supervised localization"
    - "webly supervised learning of convolutional networks"
    - "contextual action recognition with r cnn"

Connected through hyperedge HE-2 (induced by source paper "revisiting dilated convolution a simple approach for weakly and semi supervised semantic segmentation"):
  with members:
    - "incorporating network built in priors in weakly supervised semantic segmentation"
    - "adversarial complementary learning for weakly supervised object localization"
    - "self explanatory deep salient object detection"
    - "weakly supervised semantic segmentation using web crawled videos"
    - "deep self taught learning for weakly supervised object localization"
    - "object region mining with adversarial erasing a simple classification to semantic segmentation approach"
    - "two phase learning for weakly supervised object localization"
    - "stc a simple to complex framework for weakly supervised semantic segmentation"

Connected through hyperedge HE-3 (induced by source paper "transitive invariance for self supervised visual representation learning"):
  with members:
    - "colorization as a proxy task for visual understanding"
    - "unsupervised learning of depth and ego motion from video"
    - "unsupervised learning of long term motion dynamics for videos"
    - "learning representations for automatic colorization"
    - "adversarial feature learning"
    - "learning features by watching objects move"
    - "split brain autoencoders unsupervised learning by cross channel prediction"
    - "colorful image colorization"

Connected through hyperedge HE-4 (induced by source paper "unsupervised joint mining of deep features and image labels for large scale radiology image categorization and scene recognition"):
  with members:
    - "unsupervised high level feature learning by ensemble projection for semi supervised image classification and image clustering"
    - "what makes imagenet good for transfer learning"
    - "joint unsupervised learning of deep representations and image clusters"
    - "unsupervised visual representation learning by context prediction"
    - "webly supervised learning of convolutional networks"
    - "harvesting discriminative meta objects with deep cnn features for scene classification"
    - "mid level deep pattern mining"
    - "lsun construction of a large scale image dataset using deep learning with humans in the loop"

Connected through hyperedge HE-5 (induced by source paper "stc a simple to complex framework for weakly supervised semantic segmentation"):
  with members:
    - "hcp a flexible cnn framework for multi label image classification"
    - "seed expand and constrain three principles for weakly supervised image segmentation"
    - "weakly and semi supervised learning of a dcnn for semantic image segmentation"
    - "conditional random fields as recurrent neural networks"
    - "salient object detection a benchmark"
    - "fast r cnn"
    - "boxsup exploiting bounding boxes to supervise convolutional networks for semantic segmentation"
    - "constrained convolutional neural networks for weakly supervised segmentation"

Connected through hyperedge HE-6 (induced by source paper "unsupervised category discovery via looped deep pseudo task optimization using a large scale radiology image database"):
  with members:
    - "unsupervised high level feature learning by ensemble projection for semi supervised image classification and image clustering"
    - "improving computer aided detection using convolutional neural networks and random view aggregation"
    - "webly supervised learning of convolutional networks"
    - "deep filter banks for texture recognition description and segmentation"
    - "simultaneous deep transfer across domains and tasks"
    - "unsupervised learning of visual representations using videos"
    - "harvesting discriminative meta objects with deep cnn features for scene classification"
    - "automatic concept discovery from parallel text and visual corpora"

Connected through hyperedge HE-7 (induced by source paper "track and transfer watching videos to simulate strong human supervision for weakly supervised object detection"):
  with members:
    - "weakly supervised object localization with multi fold multiple instance learning"
    - "watch and learn semi supervised learning of object detectors from videos"
    - "unsupervised learning of visual representations using videos"
    - "fast r cnn"
    - "on learning to localize objects with minimal supervision"
    - "weakly supervised discovery of visual pattern configurations"
    - "spatial pyramid pooling in deep convolutional networks for visual recognition"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-8 (induced by source paper "object recognition based on amounts of unlabeled data"):
  with members:
    - "incremental gradient subgradient and proximal methods for convex optimization a survey"
    - "fractional max pooling"
    - "microsoft coco common objects in context"
    - "zero shot learning through cross modal transfer"

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)