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: "discriminatively trained and or graph models for object shape detection"
  Abstract: In this paper, we investigate a novel reconfigurable part-based model, namely And-Or graph model, to recognize object shapes in images. Our proposed model consists of four layers: leaf-nodes at the bottom are local classifiers for detecting contour fragments; or-nodes above the leaf-nodes function as the switches to activate their child leaf-nodes, making the model reconfigurable during inference; and-nodes in a higher layer capture holistic shape deformations; one root-node on the top, which is also an or-node, activates one of its child and-nodes to deal with large global variations (e.g. different poses and views). We propose a novel structural optimization algorithm to discriminatively train the And-Or model from weakly annotated data. This algorithm iteratively determines the model structures (e.g. the nodes and their layouts) along with the parameter learning. On several challenging datasets, our model demonstrates the effectiveness to perform robust shape-based object detection against background clutter and outperforms the other state-of-the-art approaches. We also release a new shape database with annotations, which includes more than   $1500$       challenging shape instances, for recognition and detection.

Connected through hyperedge HE-1 (induced by source paper "multi granularity reasoning for social relation recognition from images"):
  with members:
    - "deep reasoning with knowledge graph for social relationship understanding"
    - "simple baselines for human pose estimation and tracking"
    - "exact and consistent interpretation for piecewise linear neural networks a closed form solution"
    - "a domain based approach to social relation recognition"
    - "dual glance model for deciphering social relationships"
    - "mask r cnn"
    - "semantic object parsing with graph lstm"
    - "from facial expression recognition to interpersonal relation prediction"

Connected through hyperedge HE-2 (induced by source paper "pcn part and context information for pedestrian detection with cnns"):
  with members:
    - "learning chained deep features and classifiers for cascade in object detection"
    - "how far are we from solving pedestrian detection"
    - "a unified multi scale deep convolutional neural network for fast object detection"
    - "fused dnn a deep neural network fusion approach to fast and robust pedestrian detection"
    - "inside outside net detecting objects in context with skip pooling and recurrent neural networks"
    - "parsenet looking wider to see better"
    - "fast r cnn"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-3 (induced by source paper "aognets compositional grammatical architectures for deep learning"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "taking human out of learning applications a survey on automated machine learning"
    - "neural task planning with and or graph representations"
    - "cbam convolutional block attention module"
    - "squeezenext hardware aware neural network design"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "network dissection quantifying interpretability of deep visual representations"
    - "learning transferable architectures for scalable image recognition"

Connected through hyperedge HE-4 (induced by source paper "content adaptive sketch portrait generation by decompositional representation learning"):
  with members:
    - "cross domain visual matching via generalized similarity measure and feature learning"
    - "disc deep image saliency computing via progressive representation learning"
    - "a deep structured model with radius margin bound for 3d human activity recognition"
    - "fast r cnn"
    - "bit scalable deep hashing with regularized similarity learning for image retrieval and person re identification"
    - "image super resolution using deep convolutional networks"
    - "spatial pyramid pooling in deep convolutional networks for visual recognition"
    - "designing deep networks for surface normal estimation"

Connected through hyperedge HE-5 (induced by source paper "human pose estimation from depth images via inference embedded multi task learning"):
  with members:
    - "cost effective active learning for deep image classification"
    - "cross domain visual matching via generalized similarity measure and feature learning"
    - "dari distance metric and representation integration for person verification"
    - "deep structured scene parsing by learning with image descriptions"
    - "geometric scene parsing with hierarchical lstm"
    - "a deep structured model with radius margin bound for 3d human activity recognition"
    - "structured feature learning for pose estimation"
    - "semantic object parsing with local global long short term memory"

Connected through hyperedge HE-6 (induced by source paper "pushing the limits of deep cnns for pedestrian detection"):
  with members:
    - "filtered channel features for pedestrian detection"
    - "conditional random fields as recurrent neural networks"
    - "fast r cnn"
    - "convolutional channel features"
    - "object detection networks on convolutional feature maps"
    - "efficient piecewise training of deep structured models for semantic segmentation"
    - "learning complexity aware cascades for deep pedestrian detection"
    - "taking a deeper look at pedestrians"

Connected through hyperedge HE-7 (induced by source paper "disc deep image saliency computing via progressive representation learning"):
  with members:
    - "a deep structured model with radius margin bound for 3d human activity recognition"
    - "visual saliency based on multiscale deep features"
    - "deep feature learning with relative distance comparison for person re identification"
    - "deep joint task learning for generic object extraction"
    - "pisa pixelwise image saliency by aggregating complementary appearance contrast measures with edge preserving coherence"
    - "bit scalable deep hashing with regularized similarity learning for image retrieval and person re identification"
    - "imagenet large scale visual recognition challenge"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-8 (induced by source paper "a deep structured model with radius margin bound for 3d human activity recognition"):
  with members:
    - "3d human activity recognition with reconfigurable convolutional neural networks"
    - "long term recurrent convolutional networks for visual recognition and description"
    - "translating videos to natural language using deep recurrent neural networks"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"
    - "learning latent spatio temporal compositional model for human action recognition"
    - "joint video and text parsing for understanding events and answering queries"
    - "learning human activities and object affordances from rgb d videos"
    - "unstructured human activity detection from rgbd images"

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)