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: "convolutional models for joint object categorization and pose estimation"
  Abstract: In the task of Object Recognition, there exists a dichotomy between the categorization of objects and estimating object pose, where the former necessitates a view-invariant representation, while the latter requires a representation capable of capturing pose information over different categories of objects. With the rise of deep architectures, the prime focus has been on object category recognition. Deep learning methods have achieved wide success in this task. In contrast, object pose regression using these approaches has received relatively much less attention. In this paper we show how deep architectures, specifically Convolutional Neural Networks (CNN), can be adapted to the task of simultaneous categorization and pose estimation of objects. We investigate and analyze the layers of various CNN models and extensively compare between them with the goal of discovering how the layers of distributed representations of CNNs represent object pose information and how this contradicts with object category representations. We extensively experiment on two recent large and challenging multi-view datasets. Our models achieve better than state-of-the-art performance on both datasets.

Connected through hyperedge HE-1 (induced by source paper "unified perceptual parsing for scene understanding"):
  with members:
    - "network dissection quantifying interpretability of deep visual representations"
    - "batch renormalization towards reducing minibatch dependence in batch normalized models"
    - "interpreting deep visual representations via network dissection"
    - "learning to segment every thing"
    - "megdet a large mini batch object detector"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "multinet real time joint semantic reasoning for autonomous driving"
    - "aggregated residual transformations for deep neural networks"

Connected through hyperedge HE-2 (induced by source paper "hybrid bayesian eigenobjects combining linear subspace and deep network methods for 3d robot vision"):
  with members:
    - "shape completion enabled robotic grasping"
    - "fusionnet 3d object classification using multiple data representations"
    - "learning a probabilistic latent space of object shapes via 3d generative adversarial modeling"
    - "gift a real time and scalable 3d shape search engine"
    - "deep sliding shapes for amodal 3d object detection in rgb d images"
    - "shape and symmetry induction for 3d objects"
    - "3d shapenets a deep representation for volumetric shapes"
    - "viewpoints and keypoints"

Connected through hyperedge HE-3 (induced by source paper "what can we learn about cnns from a large scale controlled object dataset"):
  with members:
    - "deepfix a fully convolutional neural network for predicting human eye fixations"
    - "multi view convolutional neural networks for 3d shape recognition"
    - "from facial parts responses to face detection a deep learning approach"
    - "digging deep into the layers of cnns in search of how cnns achieve view invariance"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "exploring invariances in deep convolutional neural networks using synthetic images"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-4 (induced by source paper "digging deep into the layers of cnns in search of how cnns achieve view invariance"):
  with members:
    - "factorization of view object manifolds for joint object recognition and pose estimation"
    - "analyzing the performance of multilayer neural networks for object recognition"
    - "return of the devil in the details delving deep into convolutional nets"
    - "imagenet large scale visual recognition challenge"
    - "cnn features off the shelf an astounding baseline for recognition"
    - "decaf a deep convolutional activation feature for generic visual recognition"
    - "overfeat integrated recognition localization and detection using convolutional networks"
    - "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)