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: "anchornet a weakly supervised network to learn geometry sensitive features for semantic matching"
  Abstract: Despite significant progress of deep learning in recent years, state-of-the-art semantic matching methods still rely on legacy features such as SIFT or HoG. We argue that the strong invariance properties that are key to the success of recent deep architectures on the classification task make them unfit for dense correspondence tasks, unless a large amount of supervision is used. In this work, we propose a deep network, termed AnchorNet, that produces image representations that are well-suited for semantic matching. It relies on a set of filters whose response is geometrically consistent across different object instances, even in the presence of strong intra-class, scale, or viewpoint variations. Trained only with weak image-level labels, the final representation successfully captures information about the object structure and improves results of state-of-the-art semantic matching methods such as the deformable spatial pyramid or the proposal flow methods. We show positive results on the cross-instance matching task where different instances of the same object category are matched as well as on a new cross-category semantic matching task aligning pairs of instances each from a different object class.

Connected through hyperedge HE-1 (induced by source paper "learning correspondence from the cycle consistency of time"):
  with members:
    - "recycle gan unsupervised video retargeting"
    - "deep clustering for unsupervised learning of visual features"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "efficient video object segmentation via network modulation"
    - "adaptive temporal encoding network for video instance level human parsing"
    - "switchable temporal propagation network"
    - "tracking emerges by colorizing videos"
    - "representation learning with contrastive predictive coding"

Connected through hyperedge HE-2 (induced by source paper "self supervised learning for video correspondence flow"):
  with members:
    - "learning correspondence from the cycle consistency of time"
    - "long term tracking in the wild a benchmark"
    - "self supervised spatiotemporal feature learning via video rotation prediction"
    - "efficient video object segmentation via network modulation"
    - "fast online object tracking and segmentation a unifying approach"
    - "self supervised video representation learning with space time cubic puzzles"
    - "tracking emerges by colorizing videos"
    - "representation learning with contrastive predictive coding"

Connected through hyperedge HE-3 (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-4 (induced by source paper "unsupervised part based disentangling of object shape and appearance"):
  with members:
    - "synthesizing images of humans in unseen poses"
    - "deformable generator network unsupervised disentanglement of appearance and geometry"
    - "unsupervised discovery of object landmarks as structural representations"
    - "unsupervised disentangled representation learning with analogical relations"
    - "dgpose disentangled semi supervised deep generative models for human body analysis"
    - "unsupervised learning of object frames by dense equivariant image labelling"
    - "in defense of the triplet loss for person re identification"
    - "pose guided person image generation"

Connected through hyperedge HE-5 (induced by source paper "show match and segment joint learning of semantic matching and object co segmentation"):
  with members:
    - "semi supervised semantic matching"
    - "feelvos fast end to end embedding learning for video object segmentation"
    - "semantic aware attention based deep object co segmentation"
    - "recurrent transformer networks for semantic correspondence"
    - "df net unsupervised joint learning of depth and flow using cross task consistency"
    - "deepmvs learning multi view stereopsis"
    - "self supervised learning of geometrically stable features through probabilistic introspection"
    - "multimodal unsupervised image to image translation"

Connected through hyperedge HE-6 (induced by source paper "semantic attribute matching networks"):
  with members:
    - "neural best buddies sparse cross domain correspondence"
    - "recurrent transformer networks for semantic correspondence"
    - "a closed form solution to photorealistic image stylization"
    - "neighbourhood consensus networks"
    - "arbitrary style transfer with deep feature reshuffle"
    - "convolutional neural network architecture for geometric matching"
    - "diversified texture synthesis with feed forward networks"
    - "deep photo style transfer"

Connected through hyperedge HE-7 (induced by source paper "multi scale convolutions for learning context aware feature representations"):
  with members:
    - "parn pyramidal affine regression networks for dense semantic correspondence"
    - "recurrent transformer networks for semantic correspondence"
    - "end to end weakly supervised semantic alignment"
    - "convolutional neural network architecture for geometric matching"
    - "dctm discrete continuous transformation matching for semantic flow"
    - "deformable convolutional networks"
    - "understanding the effective receptive field in deep convolutional neural networks"
    - "fcss fully convolutional self similarity for dense semantic correspondence"

Connected through hyperedge HE-8 (induced by source paper "pretraining boosts out of domain robustness for pose estimation"):
  with members:
    - "an analysis of pre training on object detection"
    - "sim2real transfer learning for 3d pose estimation motion to the rescue"
    - "pifpaf composite fields for human pose estimation"
    - "deep high resolution representation learning for human pose estimation"
    - "rethinking imagenet pre training"
    - "do better imagenet models transfer better"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "simple baselines for human pose estimation and tracking"

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)