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: "deep image category discovery using a transferred similarity function"
  Abstract: Automatically discovering image categories in unlabeled natural images is one of the important goals of unsupervised learning. However, the task is challenging and even human beings define visual categories based on a large amount of prior knowledge. In this paper, we similarly utilize prior knowledge to facilitate the discovery of image categories. We present a novel end-to-end network to map unlabeled images to categories as a clustering network. We propose that this network can be learned with contrastive loss which is only based on weak binary pair-wise constraints. Such binary constraints can be learned from datasets in other domains as transferred similarity functions, which mimic a simple knowledge transfer. We first evaluate our experiments on the MNIST dataset as a proof of concept, based on predicted similarities trained on Omniglot, showing a 99\% accuracy which significantly outperforms clustering based approaches. Then we evaluate the discovery performance on Cifar-10, STL-10, and ImageNet, which achieves both state-of-the-art accuracy and shows it can be scalable to various large natural images.

Connected through hyperedge HE-1 (induced by source paper "automatically discovering and learning new visual categories with ranking statistics"):
  with members:
    - "learning to discover novel visual categories via deep transfer clustering"
    - "multi class classification without multi class labels"
    - "semi supervised learning with scarce annotations"
    - "revisiting self supervised visual representation learning"
    - "a survey on deep transfer learning"
    - "unsupervised representation learning by predicting image rotations"
    - "realistic evaluation of deep semi supervised learning algorithms"
    - "memory aware synapses learning what not to forget"

Connected through hyperedge HE-2 (induced by source paper "large scale object mining for object discovery from unlabeled video"):
  with members:
    - "unsupervised hard example mining from videos for improved object detection"
    - "computer vision for autonomous vehicles problems datasets and state of the art"
    - "in defense of the triplet loss for person re identification"
    - "object category learning and retrieval with weak supervision"
    - "track then decide category agnostic vision based multi object tracking"
    - "mask r cnn"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "inception v4 inception resnet and the impact of residual connections on learning"

Connected through hyperedge HE-3 (induced by source paper "unseen class discovery in open world classification"):
  with members:
    - "emnist an extension of mnist to handwritten letters"
    - "icarl incremental classifier and representation learning"
    - "a deep relevance matching model for ad hoc retrieval"
    - "online open world recognition"
    - "semi supervised vocabulary informed learning"
    - "towards open set deep networks"
    - "towards open world recognition"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "a probabilistic constrained clustering for transfer learning and image category discovery"):
  with members:
    - "learning to cluster in order to transfer across domains and tasks"
    - "large scale object discovery and detector adaptation from unlabeled video"
    - "semi supervised kernel metric learning using relative comparisons"
    - "identity mappings in deep residual networks"
    - "neural network based clustering using pairwise constraints"
    - "very deep convolutional networks for large scale image recognition"
    - "on constrained spectral clustering and its applications"

Connected through hyperedge HE-5 (induced by source paper "towards large scale video video object mining"):
  with members:
    - "track then decide category agnostic vision based multi object tracking"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-6 (induced by source paper "large scale object discovery and detector adaptation from unlabeled video"):
  with members:
    - "computer vision for autonomous vehicles problems datasets and state of the art"
    - "in defense of the triplet loss for person re identification"
    - "track then decide category agnostic vision based multi object tracking"
    - "time contrastive networks self supervised learning from video"
    - "sfm net learning of structure and motion from video"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "track and transfer watching videos to simulate strong human supervision for weakly supervised object detection"
    - "wider or deeper revisiting the resnet model for visual recognition"

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)