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: "object category learning and retrieval with weak supervision"
  Abstract: We consider the problem of retrieving objects from image data and learning to classify them into meaningful semantic categories with minimal supervision. To that end, we propose a fully differentiable unsupervised deep clustering approach to learn semantic classes in an end-to-end fashion without individual class labeling using only unlabeled object proposals. The key contributions of our work are 1) a kmeans clustering objective where the clusters are learned as parameters of the network and are represented as memory units, and 2) simultaneously building a feature representation, or embedding, while learning to cluster it. This approach shows promising results on two popular computer vision datasets: on CIFAR10 for clustering objects, and on the more complex and challenging Cityscapes dataset for semantically discovering classes which visually correspond to cars, people, and bicycles. Currently, the only supervision provided is segmentation objectness masks, but this method can be extended to use an unsupervised objectness-based object generation mechanism which will make the approach completely unsupervised.

Connected through hyperedge HE-1 (induced by source paper "online object representations with contrastive learning"):
  with members:
    - "self supervisory signals for object discovery and detection"
    - "dense object nets learning dense visual object descriptors by and for robotic manipulation"
    - "superpoint self supervised interest point detection and description"
    - "a self supervised learning system for object detection using physics simulation and multi view pose estimation"
    - "unsupervised object segmentation in video by efficient selection of highly probable positive features"
    - "deep image prior"
    - "mixup beyond empirical risk minimization"
    - "time contrastive networks self supervised learning from video"

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"
    - "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"
    - "ssd single shot multibox detector"

Connected through hyperedge HE-3 (induced by source paper "self supervisory signals for object discovery and detection"):
  with members:
    - "a self supervised learning system for object detection using physics simulation and multi view pose estimation"
    - "a dataset for developing and benchmarking active vision"
    - "time contrastive networks self supervised learning from video"
    - "curiosity driven exploration by self supervised prediction"
    - "speed accuracy trade offs for modern convolutional object detectors"
    - "learning features by watching objects move"
    - "unsupervised learning of visual representations using videos"
    - "deep residual learning for image 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)