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: "large scale long tailed recognition in an open world"
  Abstract: Real world data often have a long-tailed and open-ended distribution. A practical recognition system must classify among majority and minority classes, generalize from a few known instances, and acknowledge novelty upon a never seen instance. We define Open Long-Tailed Recognition (OLTR) as learning from such naturally distributed data and optimizing the classification accuracy over a balanced test set which include head, tail, and open classes. OLTR must handle imbalanced classification, few-shot learning, and open-set recognition in one integrated algorithm, whereas existing classification approaches focus only on one aspect and deliver poorly over the entire class spectrum. The key challenges are how to share visual knowledge between head and tail classes and how to reduce confusion between tail and open classes. We develop an integrated OLTR algorithm that maps an image to a feature space such that visual concepts can easily relate to each other based on a learned metric that respects the closed-world classification while acknowledging the novelty of the open world. Our so-called dynamic meta-embedding combines a direct image feature and an associated memory feature, with the feature norm indicating the familiarity to known classes. On three large-scale OLTR datasets we curate from object-centric ImageNet, scene-centric Places, and face-centric MS1M data, our method consistently outperforms the state-of-the-art. Our code, datasets, and models enable future OLTR research and are publicly available at this https URL.

Connected through hyperedge HE-1 (induced by source paper "incremental few shot object detection"):
  with members:
    - "few shot learning via saliency guided hallucination of samples"
    - "boosting few shot visual learning with self supervision"
    - "generating classification weights with gnn denoising autoencoders for few shot learning"
    - "zero shot detection"
    - "a closer look at few shot classification"
    - "continual learning a comparative study on how to defy forgetting in classification tasks"
    - "objects as points"
    - "image deformation meta networks for one shot learning"

Connected through hyperedge HE-2 (induced by source paper "few shot few shot learning and the role of spatial attention"):
  with members:
    - "dense classification and implanting for few shot learning"
    - "few shot learning with localization in realistic settings"
    - "revisiting local descriptor based image to class measure for few shot learning"
    - "finding task relevant features for few shot learning by category traversal"
    - "a closer look at few shot classification"
    - "few shot learning with metric agnostic conditional embeddings"
    - "deep clustering for unsupervised learning of visual features"
    - "learning embedding adaptation for few shot learning"

Connected through hyperedge HE-3 (induced by source paper "identifying and compensating for feature deviation in imbalanced deep learning"):
  with members:
    - "a study on action detection in the wild"
    - "simpleshot revisiting nearest neighbor classification for few shot learning"
    - "dynamic curriculum learning for imbalanced data classification"
    - "decoupling representation and classifier for long tailed recognition"
    - "push the student to learn right progressive gradient correcting by meta learner on corrupted labels"
    - "class balanced loss based on effective number of samples"
    - "meta weight net learning an explicit mapping for sample weighting"
    - "large scale fine grained categorization and domain specific transfer learning"

Connected through hyperedge HE-4 (induced by source paper "normalized and geometry aware self attention network for image captioning"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "image captioning transforming objects into words"
    - "attention on attention for image captioning"
    - "vatex a large scale high quality multilingual dataset for video and language research"
    - "multimodal transformer with multi view visual representation for image captioning"
    - "aligning linguistic words and visual semantic units for image captioning"
    - "context aware self attention networks"
    - "deep modular co attention networks for visual question answering"

Connected through hyperedge HE-5 (induced by source paper "equalization loss for long tailed object recognition"):
  with members:
    - "lvis a dataset for large vocabulary instance segmentation"
    - "decoupling representation and classifier for long tailed recognition"
    - "class balanced loss based on effective number of samples"
    - "mask scoring r cnn"
    - "meta weight net learning an explicit mapping for sample weighting"
    - "grid r cnn"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"
    - "exploring the limits of weakly supervised pretraining"

Connected through hyperedge HE-6 (induced by source paper "learning and memorizing representative prototypes for 3d point cloud semantic and instance segmentation"):
  with members:
    - "learning object bounding boxes for 3d instance segmentation on point clouds"
    - "3d bird s eye view instance segmentation"
    - "kpconv flexible and deformable convolution for point clouds"
    - "associatively segmenting instances and semantics in point clouds"
    - "memorizing normality to detect anomaly memory augmented deep autoencoder for unsupervised anomaly detection"
    - "3d instance segmentation via multi task metric learning"
    - "deep hough voting for 3d object detection in point clouds"
    - "deepgcns can gcns go as deep as cnns"

Connected through hyperedge HE-7 (induced by source paper "captioning images with novel objects via online vocabulary expansion"):
  with members:
    - "image captioning with unseen objects"
    - "pointing novel objects in image captioning"
    - "neural baby talk"
    - "deep contextualized word representations"
    - "decoupled novel object captioner"
    - "exploring visual relationship for image captioning"
    - "partially supervised image captioning"
    - "incorporating copying mechanism in image captioning for learning novel objects"

Connected through hyperedge HE-8 (induced by source paper "learning from multiple experts self paced knowledge distillation for long tailed classification"):
  with members:
    - "learning imbalanced datasets with label distribution aware margin loss"
    - "imbalance problems in object detection a review"
    - "class balanced loss based on effective number of samples"
    - "curriculumnet weakly supervised learning from large scale web images"
    - "learning to reweight examples for robust deep learning"
    - "evolutionary data measures understanding the difficulty of text classification tasks"
    - "dynamic few shot visual learning without forgetting"
    - "automated curriculum learning for neural networks"

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)