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: "unsupervised learning of object semantic parts from internal states of cnns by population encoding"
  Abstract: We address the key question of how object part representations can be found from the internal states of CNNs that are trained for high-level tasks, such as object classification. This work provides a new unsupervised method to learn semantic parts and gives new understanding of the internal representations of CNNs. Our technique is based on the hypothesis that semantic parts are represented by populations of neurons rather than by single filters. We propose a clustering technique to extract part representations, which we call Visual Concepts. We show that visual concepts are semantically coherent in that they represent semantic parts, and visually coherent in that corresponding image patches appear very similar. Also, visual concepts provide full spatial coverage of the parts of an object, rather than a few sparse parts as is typically found in keypoint annotations. Furthermore, We treat single visual concept as part detector and evaluate it for keypoint detection using the PASCAL3D+ dataset and for part detection using our newly annotated ImageNetPart dataset. The experiments demonstrate that visual concepts can be used to detect parts. We also show that some visual concepts respond to several semantic parts, provided these parts are visually similar. Thus visual concepts have the essential properties: semantic meaning and detection capability. Note that our ImageNetPart dataset gives rich part annotations which cover the whole object, making it useful for other part-related applications.

Connected through hyperedge HE-1 (induced by source paper "compositional convolutional neural networks a deep architecture with innate robustness to partial occlusion"):
  with members:
    - "combining compositional models and deep networks for robust object classification under occlusion"
    - "compositional convolutional networks for robust object classification under occlusion"
    - "tdapnet prototype network with recurrent top down attention for robust object classification under partial occlusion"
    - "greedy structure learning of hierarchical compositional models"
    - "deepvoting a robust and explainable deep network for semantic part detection under partial occlusion"
    - "visual concepts and compositional voting"
    - "interpretable convolutional neural networks"
    - "aognets compositional grammatical architectures for deep learning"

Connected through hyperedge HE-2 (induced by source paper "combining compositional models and deep networks for robust object classification under occlusion"):
  with members:
    - "greedy structure learning of hierarchical compositional models"
    - "deepvoting a robust and explainable deep network for semantic part detection under partial occlusion"
    - "interpretable convolutional neural networks"
    - "teaching compositionality to cnns"
    - "towards deep compositional networks"
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image recognition"
    - "object detectors emerge in deep scene cnns"

Connected through hyperedge HE-3 (induced by source paper "tdapnet prototype network with recurrent top down attention for robust object classification under partial occlusion"):
  with members:
    - "cutmix regularization strategy to train strong classifiers with localizable features"
    - "compositional convolutional networks for robust object classification under occlusion"
    - "robustness of object recognition under extreme occlusion in humans and computational models"
    - "convolutional neural networks with alternately updated clique"
    - "robust classification with convolutional prototype learning"
    - "deepvoting a robust and explainable deep network for semantic part detection under partial occlusion"
    - "prototypical networks for few shot learning"
    - "learning with rethinking recurrently improving convolutional neural networks through feedback"

Connected through hyperedge HE-4 (induced by source paper "compositional convolutional networks for robust object classification under occlusion"):
  with members:
    - "cutmix regularization strategy to train strong classifiers with localizable features"
    - "greedy structure learning of hierarchical compositional models"
    - "deepvoting a robust and explainable deep network for semantic part detection under partial occlusion"
    - "interpretable convolutional neural networks"
    - "improved regularization of convolutional neural networks with cutout"
    - "teaching compositionality to cnns"
    - "learning a hierarchical compositional shape vocabulary for multi class object representation"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-5 (induced by source paper "localizing occluders with compositional convolutional networks"):
  with members:
    - "compositional convolutional networks for robust object classification under occlusion"

Connected through hyperedge HE-6 (induced by source paper "semantic part detection via matching learning to generalize to novel viewpoints from limited training data"):
  with members:
    - "acquisition of localization confidence for accurate object detection"
    - "deepvoting a robust and explainable deep network for semantic part detection under partial occlusion"
    - "anchornet a weakly supervised network to learn geometry sensitive features for semantic matching"
    - "learning non maximum suppression"
    - "squeeze and excitation networks"
    - "click here human localized keypoints as guidance for viewpoint estimation"
    - "fcss fully convolutional self similarity for dense semantic correspondence"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-7 (induced by source paper "class subset selection for transfer learning using submodularity"):
  with members:
    - "what makes imagenet good for transfer learning"
    - "unsupervised learning of visual representations by solving jigsaw puzzles"
    - "context encoders feature learning by inpainting"
    - "discovering internal representations from object cnns using population encoding"
    - "unsupervised visual representation learning by context prediction"
    - "inverting visual representations with convolutional networks"
    - "unsupervised learning of visual representations using videos"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-8 (induced by source paper "net2vec quantifying and explaining how concepts are encoded by filters in deep neural networks"):
  with members:
    - "network dissection quantifying interpretability of deep visual representations"
    - "understanding deep learning requires rethinking generalization"
    - "ambient sound provides supervision for visual learning"
    - "object centric representation learning from unlabeled videos"
    - "synthesizing the preferred inputs for neurons in neural networks via deep generator networks"
    - "places an image database for deep scene understanding"
    - "do semantic parts emerge in convolutional neural networks"
    - "learning image representations tied to ego motion"

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.LG(Machine Learning)