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: "toward a taxonomy and computational models of abnormalities in images"
  Abstract: The human visual system can spot an abnormal image, and reason about what makes it strange. This task has not received enough attention in computer vision. In this paper we study various types of atypicalities in images in a more comprehensive way than has been done before. We propose a new dataset of abnormal images showing a wide range of atypicalities. We design human subject experiments to discover a coarse taxonomy of the reasons for abnormality. Our experiments reveal three major categories of abnormality: object-centric, scene-centric, and contextual. Based on this taxonomy, we propose a comprehensive computational model that can predict all different types of abnormality in images and outperform prior arts in abnormality recognition.

Connected through hyperedge HE-1 (induced by source paper "robust neural circuit reconstruction from serial electron microscopy with convolutional recurrent networks"):
  with members:
    - "bi directional cascade network for perceptual edge detection"
    - "deep predictive coding network for object recognition"
    - "recurrent residual convolutional neural network based on u net r2u net for medical image segmentation"
    - "deep predictive coding network with local recurrent processing for object recognition"
    - "learning long range spatial dependencies with horizontal gated recurrent units"
    - "do cifar 10 classifiers generalize to cifar 10"
    - "can recurrent neural networks warp time"
    - "learning what and where to attend"

Connected through hyperedge HE-2 (induced by source paper "automatic understanding of image and video advertisements"):
  with members:
    - "end to end saliency mapping via probability distribution prediction"
    - "deep reconstruction classification networks for unsupervised domain adaptation"
    - "walk and learn facial attribute representation learning from egocentric video and contextual data"
    - "what happens if learning to predict the effect of forces in images"
    - "learning aligned cross modal representations from weakly aligned data"
    - "domain separation networks"
    - "unsupervised domain adaptation with residual transfer networks"
    - "whittlesearch interactive image search with relative attribute feedback"

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)