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: "deformable part models are convolutional neural networks"
  Abstract: Deformable part models (DPMs) and convolutional neural networks (CNNs) are two widely used tools for visual recognition. They are typically viewed as distinct approaches: DPMs are graphical models (Markov random fields), while CNNs are "black-box" non-linear classifiers. In this paper, we show that a DPM can be formulated as a CNN, thus providing a novel synthesis of the two ideas. Our construction involves unrolling the DPM inference algorithm and mapping each step to an equivalent (and at times novel) CNN layer. From this perspective, it becomes natural to replace the standard image features used in DPM with a learned feature extractor. We call the resulting model DeepPyramid DPM and experimentally validate it on PASCAL VOC. DeepPyramid DPM significantly outperforms DPMs based on histograms of oriented gradients features (HOG) and slightly outperforms a comparable version of the recently introduced R-CNN detection system, while running an order of magnitude faster.

Connected through hyperedge HE-1 (induced by source paper "starnet towards weakly supervised few shot detection and explainable few shot classification"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "dense classification and implanting for few shot learning"
    - "generative modeling for small data object detection"
    - "meta learning with differentiable convex optimization"
    - "few shot learning with localization in realistic settings"
    - "finding task relevant features for few shot learning by category traversal"
    - "laso label set operations networks for multi label few shot learning"
    - "learning to find common objects across few image collections"

Connected through hyperedge HE-2 (induced by source paper "pedestrian attribute recognition a survey"):
  with members:
    - "the deeper the better analysis of person attributes recognition"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "improving person re identification by attribute and identity learning"
    - "amc automl for model compression and acceleration on mobile devices"
    - "rgb t object tracking benchmark and baseline"
    - "semstyle learning to generate stylised image captions using unaligned text"
    - "describe and attend to track learning natural language guided structural representation and visual attention for object tracking"
    - "large scale gan training for high fidelity natural image synthesis"

Connected through hyperedge HE-3 (induced by source paper "cdpm convolutional deformable part models for semantically aligned person re identification"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "deep representation learning with part loss for person re identification"
    - "video person re identification by temporal residual learning"
    - "person re identification with metric learning using privileged information"
    - "multi level factorisation net for person re identification"
    - "end to end deep kronecker product matching for person re identification"
    - "learning discriminative features with multiple granularities for person re identification"
    - "learning view specific deep networks for person re identification"

Connected through hyperedge HE-4 (induced by source paper "towards learning affine invariant representations via data efficient cnns"):
  with members:
    - "gabor convolutional networks"
    - "differentiable mpc for end to end planning and control"
    - "dynamic filtering with large sampling field for convnets"
    - "learning steerable filters for rotation equivariant cnns"
    - "rotation equivariant vector field networks"
    - "oriented response networks"
    - "prototypical networks for few shot learning"
    - "dynamic routing between capsules"

Connected through hyperedge HE-5 (induced by source paper "detector in detector multi level analysis for human parts"):
  with members:
    - "face magnet magnifying feature maps to detect small faces"
    - "aggregated channels network for real time pedestrian detection"
    - "fssd feature fusion single shot multibox detector"
    - "cms rcnn contextual multi scale region based cnn for unconstrained face detection"
    - "ai challenger a large scale dataset for going deeper in image understanding"
    - "a jointly learned deep architecture for facial attribute analysis and face detection in the wild"
    - "citypersons a diverse dataset for pedestrian detection"
    - "faceness net face detection through deep facial part responses"

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 "coupled network for robust pedestrian detection with gated multi layer feature extraction and deformable occlusion handling"):
  with members:
    - "sam rcnn scale aware multi resolution multi channel pedestrian detection"
    - "occlusion aware r cnn detecting pedestrians in a crowd"
    - "couplenet coupling global structure with local parts for object detection"
    - "citypersons a diverse dataset for pedestrian detection"
    - "what can help pedestrian detection"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "deformable convolutional networks"
    - "repulsion loss detecting pedestrians in a crowd"

Connected through hyperedge HE-8 (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"
    - "anchornet a weakly supervised network to learn geometry sensitive features for semantic matching"
    - "dctm discrete continuous transformation matching for semantic flow"
    - "deformable convolutional networks"
    - "understanding the effective receptive field in deep convolutional neural networks"

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)