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: "learning semantic part based models from google images"
  Abstract: We propose a technique to train semantic part-based models of object classes from Google Images. Our models encompass the appearance of parts and their spatial arrangement on the object, specific to each viewpoint. We learn these rich models by collecting training instances for both parts and objects, and automatically connecting the two levels. Our framework works incrementally, by learning from easy examples first, and then gradually adapting to harder ones. A key benefit of this approach is that it requires no manual part location annotations. We evaluate our models on the challenging PASCAL-Part dataset  [1]  and show how their performance increases at every step of the learning, with the final models more than doubling the performance of directly training from images retrieved by querying for part names (from 12.9 to 27.2 AP). Moreover, we show that our part models can help object detection performance by enriching the R-CNN detector with parts.

Connected through hyperedge HE-1 (induced by source paper "training object detectors from few weakly labeled and many unlabeled images"):
  with members:
    - "weakly supervised object detection with segmentation collaboration"
    - "object aware instance labeling for weakly supervised object detection"
    - "few shot object detection with attention rpn and multi relation detector"
    - "few example object detection with model communication"
    - "feature weighting and boosting for few shot segmentation"
    - "label propagation for deep semi supervised learning"
    - "adaptive masked proxies for few shot segmentation"
    - "mixmatch a holistic approach to semi supervised learning"

Connected through hyperedge HE-2 (induced by source paper "objects as context for detecting their semantic parts"):
  with members:
    - "joint multi person pose estimation and semantic part segmentation"
    - "semantic object parsing with graph lstm"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "human pose estimation via convolutional part heatmap regression"
    - "do semantic parts emerge in convolutional neural networks"
    - "subset feature learning for fine grained category classification"
    - "joint object and part segmentation using deep learned potentials"
    - "neural activation constellations unsupervised part model discovery with convolutional networks"

Connected through hyperedge HE-3 (induced by source paper "holistic instance level human parsing"):
  with members:
    - "look into person self supervised structure sensitive learning and a new benchmark for human parsing"
    - "pixelwise instance segmentation with a dynamically instantiated network"
    - "semantic object parsing with graph lstm"
    - "end to end instance segmentation and counting with recurrent attention"
    - "training region based object detectors with online hard example mining"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "pyramid scene parsing network"
    - "iterative instance segmentation"

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)