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: "end to end integration of a convolutional network deformable parts model and non maximum suppression"
  Abstract: Deformable Parts Models and Convolutional Networks each have achieved notable performance in object detection. Yet these two approaches find their strengths in complementary areas: DPMs are well-versed in object composition, modeling fine-grained spatial relationships between parts; likewise, ConvNets are adept at producing powerful image features, having been discriminatively trained directly on the pixels. In this paper, we propose a new model that combines these two approaches, obtaining the advantages of each. We train this model using a new structured loss function that considers all bounding boxes within an image, rather than isolated object instances. This enables the non-maximal suppression (NMS) operation, previously treated as a separate post-processing stage, to be integrated into the model. This allows for discriminative training of our combined Convnet + DPM + NMS model in end-to-end fashion. We evaluate our system on PASCAL VOC 2007 and 2011 datasets, achieving competitive results on both benchmarks.

Connected through hyperedge HE-1 (induced by source paper "diversity in machine learning"):
  with members:
    - "fast determinantal point processes via distortion free intermediate sampling"
    - "tagging like humans diverse and distinct image annotation"
    - "improving sequential determinantal point processes for supervised video summarization"
    - "diversity promoting bayesian learning of latent variable models"
    - "learning detection with diverse proposals"
    - "diversified texture synthesis with feed forward networks"
    - "stochastic multiple choice learning for training diverse deep ensembles"
    - "an ensemble diversity approach to supervised binary hashing"

Connected through hyperedge HE-2 (induced by source paper "trunk branch ensemble convolutional neural networks for video based face recognition"):
  with members:
    - "multi directional multi level dual cross patterns for robust face recognition"
    - "fine grained pose prediction normalization and recognition"
    - "targeting ultimate accuracy face recognition via deep embedding"
    - "robust face recognition via multimodal deep face representation"
    - "deepid3 face recognition with very deep neural networks"
    - "bilinear cnn models for fine grained visual recognition"
    - "spatial transformer networks"
    - "facenet a unified embedding for face recognition and clustering"

Connected through hyperedge HE-3 (induced by source paper "deformable part based fully convolutional network for object detection"):
  with members:
    - "instance sensitive fully convolutional networks"
    - "training region based object detectors with online hard example mining"
    - "wide residual networks"
    - "a multipath network for object detection"
    - "locnet improving localization accuracy for object detection"
    - "inside outside net detecting objects in context with skip pooling and recurrent neural networks"
    - "joint object and part segmentation using deep learned potentials"
    - "neural activation constellations unsupervised part model discovery with convolutional networks"

Connected through hyperedge HE-4 (induced by source paper "learning non maximum suppression"):
  with members:
    - "identity mappings in deep residual networks"
    - "ssd single shot multibox detector"
    - "learning convolutional neural networks for graphs"
    - "end to end training of object class detectors for mean average precision"
    - "object localization in imagenet by looking out of the window"
    - "you only look once unified real time object detection"
    - "fast r cnn"
    - "a convnet for non maximum suppression"

Connected through hyperedge HE-5 (induced by source paper "learning chained deep features and classifiers for cascade in object detection"):
  with members:
    - "wide residual networks"
    - "xception deep learning with depthwise separable convolutions"
    - "craft objects from images"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "fast r cnn"
    - "deepid net deformable deep convolutional neural networks for object detection"
    - "going deeper with convolutions"

Connected through hyperedge HE-6 (induced by source paper "learning detection with diverse proposals"):
  with members:
    - "ssd single shot multibox detector"
    - "fast r cnn"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "spatial semantic regularisation for large scale object detection"
    - "return of the devil in the details delving deep into convolutional nets"
    - "caffe convolutional architecture for fast feature embedding"
    - "overfeat integrated recognition localization and detection using convolutional networks"
    - "visualizing and understanding convolutional networks"

Connected through hyperedge HE-7 (induced by source paper "counting everyday objects in everyday scenes"):
  with members:
    - "analyzing the behavior of visual question answering models"
    - "end to end instance segmentation and counting with recurrent attention"
    - "ssd single shot multibox detector"
    - "clevr a diagnostic dataset for compositional language and elementary visual reasoning"
    - "salient object subitizing"
    - "multimodal compact bilinear pooling for visual question answering and visual grounding"
    - "exploring models and data for image question answering"
    - "you only look once unified real time object detection"

Connected through hyperedge HE-8 (induced by source paper "action temporal localization in untrimmed videos via multi stage cnns"):
  with members:
    - "fast r cnn"
    - "advances in human action recognition a survey"
    - "objects2action classifying and localizing actions without any video example"
    - "learning to track for spatio temporal action localization"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "deepbox learning objectness with convolutional networks"
    - "very deep convolutional networks for large scale image recognition"
    - "learning spatiotemporal features with 3d convolutional 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)