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: "efficient on the fly category retrieval using convnets and gpus"
  Abstract: We investigate the gains in precision and speed, that can be obtained by using Convolutional Networks (ConvNets) for on-the-fly retrieval - where classifiers are learnt at run time for a textual query from downloaded images, and used to rank large image or video datasets. #R##N#We make three contributions: (i) we present an evaluation of state-of-the-art image representations for object category retrieval over standard benchmark datasets containing 1M+ images; (ii) we show that ConvNets can be used to obtain features which are incredibly performant, and yet much lower dimensional than previous state-of-the-art image representations, and that their dimensionality can be reduced further without loss in performance by compression using product quantization or binarization. Consequently, features with the state-of-the-art performance on large-scale datasets of millions of images can fit in the memory of even a commodity GPU card; (iii) we show that an SVM classifier can be learnt within a ConvNet framework on a GPU in parallel with downloading the new training images, allowing for a continuous refinement of the model as more images become available, and simultaneous training and ranking. The outcome is an on-the-fly system that significantly outperforms its predecessors in terms of: precision of retrieval, memory requirements, and speed, facilitating accurate on-the-fly learning and ranking in under a second on a single GPU.

Connected through hyperedge HE-1 (induced by source paper "subic a supervised structured binary code for image search"):
  with members:
    - "billion scale similarity search with gpus"
    - "deep image retrieval learning global representations for image search"
    - "approximate search with quantized sparse representations"
    - "how should we evaluate supervised hashing"
    - "learning to hash with binary deep neural network"
    - "deep semantic ranking based hashing for multi label image retrieval"
    - "simultaneous feature learning and hash coding with deep neural networks"
    - "bit scalable deep hashing with regularized similarity learning for image retrieval and person re identification"

Connected through hyperedge HE-2 (induced by source paper "a deeper look at dataset bias"):
  with members:
    - "analyzing the performance of multilayer neural networks for object recognition"
    - "return of the devil in the details delving deep into convolutional nets"
    - "part based r cnns for fine grained category detection"
    - "lsda large scale detection through adaptation"
    - "imagenet large scale visual recognition challenge"
    - "multi scale orderless pooling of deep convolutional activation features"
    - "cnn features off the shelf an astounding baseline for recognition"
    - "a testbed for cross dataset analysis"

Connected through hyperedge HE-3 (induced by source paper "vrfp on the fly video retrieval using web images and fast fisher vector products"):
  with members:
    - "exploiting feature and class relationships in video categorization with regularized deep neural networks"
    - "sift meets cnn a decade survey of instance retrieval"
    - "eventnet a large scale structured concept library for complex event detection in video"
    - "selecting relevant web trained concepts for automated event retrieval"
    - "tagbook a semantic video representation without supervision for event detection"
    - "objects2action classifying and localizing actions without any video example"
    - "temporal localization of fine grained actions in videos by domain transfer from web images"
    - "building a large concept bank for representing events in video"

Connected through hyperedge HE-4 (induced by source paper "visual instance retrieval with deep convolutional networks"):
  with members:
    - "netvlad cnn architecture for weakly supervised place recognition"
    - "exploiting local features from deep networks for image retrieval"
    - "very deep convolutional networks for large scale image recognition"
    - "object detectors emerge in deep scene cnns"
    - "return of the devil in the details delving deep into convolutional nets"
    - "a baseline for visual instance retrieval with deep convolutional networks"
    - "factors of transferability for a generic convnet representation"
    - "neural codes for image retrieval"

Connected through hyperedge HE-5 (induced by source paper "a baseline for visual instance retrieval with deep convolutional networks"):
  with members:
    - "very deep convolutional networks for large scale image recognition"
    - "return of the devil in the details delving deep into convolutional nets"
    - "neural codes for image retrieval"
    - "descriptor matching with convolutional neural networks a comparison to sift"
    - "cnn features off the shelf an astounding baseline for recognition"
    - "decaf a deep convolutional activation feature for generic visual recognition"
    - "visualizing and understanding convolutional networks"

Connected through hyperedge HE-6 (induced by source paper "stacked quantizers for compositional vector compression"):
  with members:
    - "return of the devil in the details delving deep into convolutional nets"
    - "visualizing and understanding 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)