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 poselets for human detection"
  Abstract: We address the problem of detecting people in natural scenes using a part approach based on poselets. We propose a bootstrapping method that allows us to collect millions of weakly labeled examples for each poselet type. We use these examples to train a Convolutional Neural Net to discriminate different poselet types and separate them from the background class. We then use the trained CNN as a way to represent poselet patches with a Pose Discriminative Feature (PDF) vector -- a compact 256-dimensional feature vector that is effective at discriminating pose from appearance. We train the poselet model on top of PDF features and combine them with object-level CNNs for detection and bounding box prediction. The resulting model leads to state-of-the-art performance for human detection on the PASCAL datasets.

Connected through hyperedge HE-1 (induced by source paper "pedestrian attribute recognition a survey"):
  with members:
    - "the deeper the better analysis of person attributes recognition"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "improving person re identification by attribute and identity learning"
    - "amc automl for model compression and acceleration on mobile devices"
    - "rgb t object tracking benchmark and baseline"
    - "semstyle learning to generate stylised image captions using unaligned text"
    - "describe and attend to track learning natural language guided structural representation and visual attention for object tracking"
    - "large scale gan training for high fidelity natural image synthesis"

Connected through hyperedge HE-2 (induced by source paper "action driven object detection with top down visual attentions"):
  with members:
    - "ssd single shot multibox detector"
    - "improving object detection with deep convolutional networks via bayesian optimization and structured prediction"
    - "spatial transformer networks"
    - "attentionnet aggregating weak directions for accurate object detection"
    - "show attend and tell neural image caption generation with visual attention"
    - "deep residual learning for image recognition"
    - "fast r cnn"
    - "draw a recurrent neural network for image generation"

Connected through hyperedge HE-3 (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"
    - "mid level elements for object 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-4 (induced by source paper "attentionnet aggregating weak directions for accurate object detection"):
  with members:
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "analyzing the performance of multilayer neural networks for object recognition"
    - "joint training of a convolutional network and a graphical model for human pose estimation"
    - "deepid net deformable deep convolutional neural networks for object detection"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "return of the devil in the details delving deep into convolutional nets"
    - "spatial pyramid pooling in deep convolutional networks for visual recognition"

Connected through hyperedge HE-5 (induced by source paper "evaluation of deep convolutional nets for document image classification and retrieval"):
  with members:
    - "bird species categorization using pose normalized deep convolutional nets"
    - "neural codes for image retrieval"
    - "cnn features off the shelf an astounding baseline for recognition"

Connected through hyperedge HE-6 (induced by source paper "actions and attributes from wholes and parts"):
  with members:
    - "joint training of a convolutional network and a graphical model for human pose estimation"
    - "very deep convolutional networks for large scale image recognition"
    - "part based r cnns for fine grained category detection"
    - "bird species categorization using pose normalized deep convolutional nets"
    - "caffe convolutional architecture for fast feature embedding"
    - "deformable part models are convolutional neural networks"
    - "deeppose human pose estimation via deep neural networks"
    - "overfeat integrated recognition localization and detection using 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)