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: "mix and match tuning for self supervised semantic segmentation"
  Abstract: Deep convolutional networks for semantic image segmentation typically require large-scale labeled data, e.g. ImageNet and MS COCO, for network pre-training. To reduce annotation efforts, self-supervised semantic segmentation is recently proposed to pre-train a network without any human-provided labels. The key of this new form of learning is to design a proxy task (e.g. image colorization), from which a discriminative loss can be formulated on unlabeled data. Many proxy tasks, however, lack the critical supervision signals that could induce discriminative representation for the target image segmentation task. Thus self-supervision's performance is still far from that of supervised pre-training. In this study, we overcome this limitation by incorporating a "mix-and-match" (M&M) tuning stage in the self-supervision pipeline. The proposed approach is readily pluggable to many self-supervision methods and does not use more annotated samples than the original process. Yet, it is capable of boosting the performance of target image segmentation task to surpass fully-supervised pre-trained counterpart. The improvement is made possible by better harnessing the limited pixel-wise annotations in the target dataset. Specifically, we first introduce the "mix" stage, which sparsely samples and mixes patches from the target set to reflect rich and diverse local patch statistics of target images. A "match" stage then forms a class-wise connected graph, which can be used to derive a strong triplet-based discriminative loss for fine-tuning the network. Our paradigm follows the standard practice in existing self-supervised studies and no extra data or label is required. With the proposed M&M approach, for the first time, a self-supervision method can achieve comparable or even better performance compared to its ImageNet pre-trained counterpart on both PASCAL VOC2012 dataset and CityScapes dataset.

Connected through hyperedge HE-1 (induced by source paper "extending maps with semantic and contextual object information for robot navigation a learning based framework using visual and depth cues"):
  with members:
    - "kaolin a pytorch library for accelerating 3d deep learning research"
    - "yolact real time instance segmentation"
    - "mapping road lanes using laser remission and deep neural networks"
    - "end to end recovery of human shape and pose"
    - "tracking the untrackable learning to track multiple cues with long term dependencies"
    - "pedestrian prediction by planning using deep neural networks"
    - "mask r cnn"
    - "joint 2d 3d semantic data for indoor scene understanding"

Connected through hyperedge HE-2 (induced by source paper "auto annotation quality prediction for semi supervised learning with ensembles"):
  with members:
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "snapshot ensembles train 1 get m for free"
    - "data distillation towards omni supervised learning"
    - "checkpoint ensembles ensemble methods from a single training process"
    - "a review on deep learning techniques applied to semantic segmentation"
    - "rethinking atrous convolution for semantic image segmentation"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "temporal ensembling for semi supervised learning"

Connected through hyperedge HE-3 (induced by source paper "consensus driven propagation in massive unlabeled data for face recognition"):
  with members:
    - "the devil of face recognition is in the noise"
    - "accelerated training for massive classification via dynamic class selection"
    - "arcface additive angular margin loss for deep face recognition"
    - "pose robust face recognition via deep residual equivariant mapping"
    - "deep imbalanced learning for face recognition and attribute prediction"
    - "semi supervised sparse representation based classification for face recognition with insufficient labeled samples"
    - "learning transferable architectures for scalable image recognition"
    - "unsupervised domain adaptation for face recognition in unlabeled videos"

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)