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: "do convnets learn correspondence"
  Abstract: Convolutional neural nets (convnets) trained from massive labeled datasets have substantially improved the state-of-the-art in image classification and object detection. However, visual understanding requires establishing correspondence on a finer level than object category. Given their large pooling regions and training from whole-image labels, it is not clear that convnets derive their success from an accurate correspondence model which could be used for precise localization. In this paper, we study the effectiveness of convnet activation features for tasks requiring correspondence. We present evidence that convnet features localize at a much finer scale than their receptive field sizes, that they can be used to perform intraclass alignment as well as conventional hand-engineered features, and that they outperform conventional features in keypoint prediction on objects from PASCAL VOC 2011.

Connected through hyperedge HE-1 (induced by source paper "finding and visualizing weaknesses of deep reinforcement learning agents"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "adversarial policies attacking deep reinforcement learning"
    - "local explanation methods for deep neural networks lack sensitivity to parameter values"
    - "establishing appropriate trust via critical states"
    - "rigorous agent evaluation an adversarial approach to uncover catastrophic failures"
    - "sanity checks for saliency maps"
    - "understanding regularization to visualize convolutional neural networks"
    - "evaluating feature importance estimates"

Connected through hyperedge HE-2 (induced by source paper "learning semantic correspondence exploiting an object level prior"):
  with members:
    - "sfnet learning object aware semantic correspondence"
    - "hyperpixel flow semantic correspondence with multi layer neural features"
    - "learning correspondence from the cycle consistency of time"
    - "recurrent transformer networks for semantic correspondence"
    - "df net unsupervised joint learning of depth and flow using cross task consistency"
    - "self supervised learning of geometrically stable features through probabilistic introspection"
    - "neighbourhood consensus networks"
    - "attentive semantic alignment with offset aware correlation kernels"

Connected through hyperedge HE-3 (induced by source paper "learning video representations from correspondence proposals"):
  with members:
    - "eco efficient convolutional network for online video understanding"
    - "end to end learning of motion representation for video understanding"
    - "flownet3d learning scene flow in 3d point clouds"
    - "motion feature network fixed motion filter for action recognition"
    - "deep sets"
    - "dynamic graph cnn for learning on point clouds"
    - "motion fused frames data level fusion strategy for hand gesture recognition"
    - "quo vadis action recognition a new model and the kinetics dataset"

Connected through hyperedge HE-4 (induced by source paper "canonical surface mapping via geometric cycle consistency"):
  with members:
    - "learning correspondence from the cycle consistency of time"
    - "learning category specific mesh reconstruction from image collections"
    - "unsupervised geometry aware representation for 3d human pose estimation"
    - "unsupervised learning of shape and pose with differentiable point clouds"
    - "multi view consistency as supervisory signal for learning shape and pose prediction"
    - "densepose dense human pose estimation in the wild"
    - "unsupervised learning of object frames by dense equivariant image labelling"
    - "transitive invariance for self supervised visual representation learning"

Connected through hyperedge HE-5 (induced by source paper "from handcrafted to deep local features"):
  with members:
    - "local descriptors optimized for average precision"
    - "large scale evaluation of local image feature detectors on homography datasets"
    - "superpoint self supervised interest point detection and description"
    - "learning spread out local feature descriptors"
    - "hpatches a benchmark and evaluation of handcrafted and learned local descriptors"
    - "lift learned invariant feature transform"
    - "quad networks unsupervised learning to rank for interest point detection"
    - "learning deep embeddings with histogram loss"

Connected through hyperedge HE-6 (induced by source paper "hyperpixel flow semantic correspondence with multi layer neural features"):
  with members:
    - "semantic matching by weakly supervised 2d point set registration"
    - "exploring randomly wired neural networks for image recognition"
    - "parn pyramidal affine regression networks for dense semantic correspondence"
    - "recurrent transformer networks for semantic correspondence"
    - "self supervised learning of geometrically stable features through probabilistic introspection"
    - "neighbourhood consensus networks"
    - "hierarchical metric learning and matching for 2d and 3d geometric correspondences"
    - "attentive semantic alignment with offset aware correlation kernels"

Connected through hyperedge HE-7 (induced by source paper "low cost transfer learning of face tasks"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "multi task convolutional neural network for pose invariant face recognition"
    - "taskonomy disentangling task transfer learning"
    - "network dissection quantifying interpretability of deep visual representations"
    - "heterogeneous face attribute estimation a deep multi task learning approach"
    - "learning spatial regularization with image level supervisions for multi label image classification"
    - "synthesizing the preferred inputs for neurons in neural networks via deep generator networks"
    - "multifaceted feature visualization uncovering the different types of features learned by each neuron in deep neural networks"

Connected through hyperedge HE-8 (induced by source paper "unsupervised learning of landmarks by descriptor vector exchange"):
  with members:
    - "learning landmarks from unaligned data using image translation"
    - "unsupervised learning of object landmarks through conditional image generation"
    - "densereg fully convolutional dense shape regression in the wild"
    - "self supervised learning of a facial attribute embedding from video"
    - "wing loss for robust facial landmark localisation with convolutional neural networks"
    - "self supervised learning of geometrically stable features through probabilistic introspection"
    - "unsupervised discovery of object landmarks as structural representations"
    - "voxceleb2 deep speaker 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)