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 spatial pyramid the devil is once again in the details"
  Abstract: In this paper we show that by carefully making good choices for various detailed but important factors in a visual recognition framework using deep learning features, one can achieve a simple, efficient, yet highly accurate image classification system. We first list 5 important factors, based on both existing researches and ideas proposed in this paper. These important detailed factors include: 1) ‘2 matrix normalization is more effective than unnormalized or‘2 vector normalization, 2) the proposed natural deep spatial pyramid is very effective, and 3) a very small K in Fisher Vectors surprisingly achieves higher accuracy than normally used large K values. Along with other choices (convolutional activations and multiple scales), the proposed DSP framework is not only intuitive and efficient, but also achieves excellent classification accuracy on many benchmark datasets. For example, DSP’s accuracy on SUN397 is 59.78%, significantly higher than previous state-of-the-art (53.86%).

Connected through hyperedge HE-1 (induced by source paper "knowledge guided disambiguation for large scale scene classification with multi resolution cnns"):
  with members:
    - "hybrid cnn and dictionary based models for scene recognition and domain adaptation"
    - "weakly supervised patchnets describing and aggregating local patches for scene recognition"
    - "locally supervised deep hybrid model for scene recognition"
    - "transferring object scene convolutional neural networks for event recognition in still images"
    - "real time action recognition with enhanced motion vector cnns"
    - "learning contextual dependence with convolutional hierarchical recurrent neural networks"
    - "places an image database for deep scene understanding"
    - "weakly supervised fine grained categorization with part based image representation"

Connected through hyperedge HE-2 (induced by source paper "locally supervised deep hybrid model for scene recognition"):
  with members:
    - "hybrid cnn and dictionary based models for scene recognition and domain adaptation"
    - "knowledge guided disambiguation for large scale scene classification with multi resolution cnns"
    - "weakly supervised patchnets describing and aggregating local patches for scene recognition"
    - "text attentional convolutional neural network for scene text detection"
    - "learning contextual dependence with convolutional hierarchical recurrent neural networks"
    - "learning deep representation for face alignment with auxiliary attributes"
    - "detecting text in natural image with connectionist text proposal network"
    - "local color contrastive descriptor for image classification"

Connected through hyperedge HE-3 (induced by source paper "weakly supervised patchnets describing and aggregating local patches for scene recognition"):
  with members:
    - "hybrid cnn and dictionary based models for scene recognition and domain adaptation"
    - "knowledge guided disambiguation for large scale scene classification with multi resolution cnns"
    - "locally supervised deep hybrid model for scene recognition"
    - "transferring object scene convolutional neural networks for event recognition in still images"
    - "learning contextual dependence with convolutional hierarchical recurrent neural networks"
    - "actionness estimation using hybrid fully convolutional networks"
    - "temporal segment networks towards good practices for deep action recognition"
    - "object scene convolutional neural networks for event recognition in images"

Connected through hyperedge HE-4 (induced by source paper "visual concept recognition and localization via iterative introspection"):
  with members:
    - "weakly supervised fine grained categorization with part based image representation"
    - "fine grained pose prediction normalization and recognition"
    - "bilinear cnn models for fine grained visual recognition"
    - "spatial transformer networks"
    - "neural activation constellations unsupervised part model discovery with convolutional networks"
    - "the unreasonable effectiveness of noisy data for fine grained recognition"
    - "active object localization with deep reinforcement learning"
    - "learning deep features for discriminative localization"

Connected through hyperedge HE-5 (induced by source paper "action classification via concepts and attributes"):
  with members:
    - "network dissection quantifying interpretability of deep visual representations"
    - "weakly supervised object localization with multi fold multiple instance learning"
    - "acd action concept discovery from image sentence corpora"
    - "visual genome connecting language and vision using crowdsourced dense image annotations"
    - "learning models for actions and person object interactions with transfer to question answering"
    - "weakly supervised deep detection networks"
    - "deep residual learning for image recognition"
    - "constrained convolutional neural networks for weakly supervised segmentation"

Connected through hyperedge HE-6 (induced by source paper "scene recognition with cnns objects scales and dataset bias"):
  with members:
    - "harvesting discriminative meta objects with deep cnn features for scene classification"
    - "multi scale recognition with dag cnns"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "object detectors emerge in deep scene cnns"
    - "caffe convolutional architecture for fast feature embedding"
    - "multi scale orderless pooling of deep convolutional activation features"
    - "cnn features off the shelf an astounding baseline for recognition"

Connected through hyperedge HE-7 (induced by source paper "better exploiting os cnns for better event recognition in images"):
  with members:
    - "object scene convolutional neural networks for event recognition in images"
    - "deep filter banks for texture recognition description and segmentation"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "towards good practices for very deep two stream convnets"
    - "places205 vggnet models for scene recognition"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "action recognition with trajectory pooled deep convolutional descriptors"
    - "going deeper with convolutions"

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)