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: "deep carving discovering visual attributes by carving deep neural nets"
  Abstract: Most of the approaches for discovering visual attributes in images demand significant supervision, which is cumbersome to obtain. In this paper, we aim to discover visual attributes in a weakly supervised setting that is commonly encountered with contemporary image search engines. Deep Convolutional Neural Networks (CNNs) have enjoyed remarkable success in vision applications recently. However, in a weakly supervised scenario, widely used CNN training procedures do not learn a robust model for predicting multiple attribute labels simultaneously. The primary reason is that the attributes highly co-occur within the training data. To ameliorate this limitation, we propose Deep-Carving, a novel training procedure with CNNs, that helps the net efficiently carve itself for the task of multiple attribute prediction. During training, the responses of the feature maps are exploited in an ingenious way to provide the net with multiple pseudo-labels (for training images) for subsequent iterations. The process is repeated periodically after a fixed number of iterations, and enables the net carve itself iteratively for efficiently disentangling features. Additionally, we contribute a noun-adjective pairing inspired Natural Scenes Attributes Dataset to the research community, CAMIT - NSAD, containing a number of co-occurring attributes within a noun category. We describe, in detail, salient aspects of this dataset. Our experiments on CAMIT-NSAD and the SUN Attributes Dataset, with weak supervision, clearly demonstrate that the Deep-Carved CNNs consistently achieve considerable improvement in the precision of attribute prediction over popular baseline methods.

Connected through hyperedge HE-1 (induced by source paper "joint attention in driver pedestrian interaction from theory to practice"):
  with members:
    - "when vehicles see pedestrians with phones a multi cue framework for recognizing phone based activities of pedestrians"
    - "video salient object detection via fully convolutional networks"
    - "agreeing to cross how drivers and pedestrians communicate"
    - "citypersons a diverse dataset for pedestrian detection"
    - "integrating three mechanisms of visual attention for active visual search"
    - "learning where to attend like a human driver"
    - "real time human motion capture with multiple depth cameras"
    - "hcp a flexible cnn framework for multi label image classification"

Connected through hyperedge HE-2 (induced by source paper "the devil is in the middle exploiting mid level representations for cross domain instance matching"):
  with members:
    - "improving person re identification by attribute and identity learning"
    - "network dissection quantifying interpretability of deep visual representations"
    - "in defense of the triplet loss for person re identification"
    - "deeply learned part aligned representations for person re identification"
    - "scalable person re identification on supervised smoothed manifold"
    - "person re identification by deep joint learning of multi loss classification"
    - "divide and fuse a re ranking approach for person re identification"
    - "hydraplus net attentive deep features for pedestrian analysis"

Connected through hyperedge HE-3 (induced by source paper "improved inception residual convolutional neural network for object recognition"):
  with members:
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "inception recurrent convolutional neural network for object recognition"
    - "aggregated residual transformations for deep neural networks"
    - "identity mappings in deep residual networks"
    - "cnn architectures for large scale audio classification"
    - "the microsoft 2016 conversational speech recognition system"
    - "wide residual networks"
    - "wavenet a generative model for raw audio"

Connected through hyperedge HE-4 (induced by source paper "deeppermnet visual permutation learning"):
  with members:
    - "shuffle and learn unsupervised learning using temporal order verification"
    - "adversarial feature learning"
    - "unsupervised learning of visual representations by solving jigsaw puzzles"
    - "context encoders feature learning by inpainting"
    - "colorful image colorization"
    - "end to end localization and ranking for relative attributes"
    - "self supervised video representation learning with odd one out networks"
    - "unsupervised visual representation learning by context prediction"

Connected through hyperedge HE-5 (induced by source paper "deepnav learning to navigate large cities"):
  with members:
    - "planet photo geolocation with convolutional neural networks"
    - "target driven visual navigation in indoor scenes using deep reinforcement learning"
    - "asynchronous methods for deep reinforcement learning"
    - "control of memory active perception and action in minecraft"
    - "learning to navigate in complex environments"
    - "visual saliency based on multiscale deep features"
    - "unsupervised learning of edges"
    - "conditional random fields as recurrent neural networks"

Connected through hyperedge HE-6 (induced by source paper "query free clothing retrieval via implicit relevance feedback"):
  with members:
    - "cross domain image retrieval with a dual attribute aware ranking network"
    - "part stacked cnn for fine grained visual categorization"
    - "multi task cnn model for attribute prediction"
    - "going deeper with convolutions"
    - "large scale visual recommendations from street fashion images"
    - "cnn features off the shelf an astounding baseline for recognition"
    - "panda pose aligned networks for deep attribute modeling"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

Connected through hyperedge HE-7 (induced by source paper "cvpaper challenge in 2015 a review of cvpr2015 and deepsurvey"):
  with members:
    - "semi supervised learning with explicit relationship regularization"
    - "understanding image virality"
    - "watch and learn semi supervised learning of object detectors from videos"
    - "improving object detection with deep convolutional networks via bayesian optimization and structured prediction"
    - "bilinear random projections for locality sensitive binary codes"
    - "learning multiple visual tasks while discovering their structure"
    - "facenet a unified embedding for face recognition and clustering"
    - "background subtraction via generalized fused lasso foreground modeling"

Connected through hyperedge HE-8 (induced by source paper "automatic attribute discovery with neural activations"):
  with members:
    - "a deep learning based fast image saliency detection algorithm"
    - "understanding neural networks through deep visualization"
    - "robust image sentiment analysis using progressively trained and domain transferred deep networks"
    - "visual affect around the world a large scale multilingual visual sentiment ontology"
    - "automatic concept discovery from parallel text and visual corpora"
    - "neural activation constellations unsupervised part model discovery with convolutional networks"
    - "deep residual learning for image recognition"
    - "deep learning for object saliency detection and image 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)