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: "iterative reorganization with weak spatial constraints solving arbitrary jigsaw puzzles for unsupervised representation learning"
  Abstract: Learning visual features from unlabeled image data is an important yet challenging task, which is often achieved by training a model on some annotation-free information. We consider spatial contexts, for which we solve so-called jigsaw puzzles, i.e., each image is cut into grids and then disordered, and the goal is to recover the correct configuration. Existing approaches formulated it as a classification task by defining a fixed mapping from a small subset of configurations to a class set, but these approaches ignore the underlying relationship between different configurations and also limit their application to more complex scenarios. This paper presents a novel approach which applies to jigsaw puzzles with an arbitrary grid size and dimensionality. We provide a fundamental and generalized principle, that weaker cues are easier to be learned in an unsupervised manner and also transfer better. In the context of puzzle recognition, we use an iterative manner which, instead of solving the puzzle all at once, adjusts the order of the patches in each step until convergence. In each step, we combine both unary and binary features on each patch into a cost function judging the correctness of the current configuration. Our approach, by taking similarity between puzzles into consideration, enjoys a more reasonable way of learning visual knowledge. We verify the effectiveness of our approach in two aspects. First, it is able to solve arbitrarily complex puzzles, including high-dimensional puzzles, that prior methods are difficult to handle. Second, it serves as a reliable way of network initialization, which leads to better transfer performance in a few visual recognition tasks including image classification, object detection, and semantic segmentation.

Connected through hyperedge HE-1 (induced by source paper "fine grained visual classification via progressive multi granularity training of jigsaw patches"):
  with members:
    - "the devil is in the channels mutual channel loss for fine grained image classification"
    - "singan learning a generative model from a single natural image"
    - "cross x learning for fine grained visual categorization"
    - "weakly supervised complementary parts models for fine grained image classification from the bottom up"
    - "learning to navigate for fine grained classification"
    - "a fully progressive approach to single image super resolution"
    - "a style based generator architecture for generative adversarial networks"
    - "progressive growing of gans for improved quality stability and variation"

Connected through hyperedge HE-2 (induced by source paper "towards a hypothesis on visual transformation based self supervision"):
  with members:
    - "scaling and benchmarking self supervised visual representation learning"
    - "aet vs aed unsupervised representation learning by auto encoding transformations rather than data"
    - "self supervised dialogue learning"
    - "contrastive multiview coding"
    - "self supervised exploration via disagreement"
    - "using self supervised learning can improve model robustness and uncertainty"
    - "the visual task adaptation benchmark"
    - "self supervised visual feature learning with deep neural networks a survey"

Connected through hyperedge HE-3 (induced by source paper "information based deep clustering an experimental study"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "self supervised visual feature learning with deep neural networks a survey"
    - "boosting self supervised learning via knowledge transfer"
    - "sentinet detecting physical attacks against deep learning systems"
    - "jointly optimize data augmentation and network training adversarial data augmentation in human pose estimation"
    - "deep clustering for unsupervised learning of visual features"
    - "mine mutual information neural estimation"
    - "video jigsaw unsupervised learning of spatiotemporal context for video action 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)