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: "mid level elements for object detection"
  Abstract: Building on the success of recent discriminative mid-level elements, we propose a surprisingly simple approach for object detection which performs comparable to the current state-of-the-art approaches on PASCAL VOC comp-3 detection challenge (no external data). Through extensive experiments and ablation analysis, we show how our approach effectively improves upon the HOG-based pipelines by adding an intermediate mid-level representation for the task of object detection. This representation is easily interpretable and allows us to visualize what our object detector "sees". We also discuss the insights our approach shares with CNN-based methods, such as sharing representation between categories helps.

Connected through hyperedge HE-1 (induced by source paper "semi supervised co analysis of 3d shape styles from projected lines"):
  with members:
    - "data driven shape analysis and processing"
    - "multi view convolutional neural networks for 3d shape recognition"
    - "mid level deep pattern mining"
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image recognition"
    - "multi scale orderless pooling of deep convolutional activation features"
    - "cnn features off the shelf an astounding baseline for recognition"
    - "decaf a deep convolutional activation feature for generic visual recognition"

Connected through hyperedge HE-2 (induced by source paper "deepcamp deep convolutional action attribute mid level patterns"):
  with members:
    - "contextual action recognition with r cnn"
    - "mid level deep pattern mining"
    - "fast r cnn"
    - "deep poselets for human detection"
    - "automatic discovery and optimization of parts for image classification"
    - "part based r cnns for fine grained category detection"
    - "r cnns for pose estimation and action detection"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-3 (induced by source paper "mining mid level visual patterns with deep cnn activations"):
  with members:
    - "hcp a flexible cnn framework for multi label image classification"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "cnn rnn a unified framework for multi label image classification"
    - "deepcamp deep convolutional action attribute mid level patterns"
    - "xnor net imagenet classification using binary convolutional neural networks"
    - "modeling visual compatibility through hierarchical mid level elements"
    - "mining discriminative triplets of patches for fine grained classification"
    - "inverting visual representations with 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)