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: "information pursuit a bayesian framework for sequential scene parsing"
  Abstract: Despite enormous progress in object detection and classification, the problem of incorporating expected contextual relationships among object instances into modern recognition systems remains a key challenge. In this work we propose Information Pursuit, a Bayesian framework for scene parsing that combines prior models for the geometry of the scene and the spatial arrangement of objects instances with a data model for the output of high-level image classifiers trained to answer specific questions about the scene. In the proposed framework, the scene interpretation is progressively refined as evidence accumulates from the answers to a sequence of questions. At each step, we choose the question to maximize the mutual information between the new answer and the full interpretation given the current evidence obtained from previous inquiries. We also propose a method for learning the parameters of the model from synthesized, annotated scenes obtained by top-down sampling from an easy-to-learn generative scene model. Finally, we introduce a database of annotated indoor scenes of dining room tables, which we use to evaluate the proposed approach.

Connected through hyperedge HE-1 (induced by source paper "visual semantic information pursuit a survey"):
  with members:
    - "linknet relational embedding for scene graph"
    - "iterative visual reasoning beyond convolutions"
    - "graph r cnn for scene graph generation"
    - "zoom net mining deep feature interactions for visual relationship recognition"
    - "large scale visual relationship understanding"
    - "structure inference net object detection using scene level context and instance level relationships"
    - "visual relationship detection based on guided proposals and semantic knowledge distillation"
    - "factorizable net an efficient subgraph based framework for scene graph generation"

Connected through hyperedge HE-2 (induced by source paper "rgb d individual segmentation"):
  with members:
    - "lf net learning local features from images"
    - "srda generating instance segmentation annotation via scanning reasoning and domain adaptation"
    - "applying domain randomization to synthetic data for object category detection"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "neighbourhood consensus networks"
    - "semi parametric image synthesis"
    - "path aggregation network for instance segmentation"
    - "sniper efficient multi scale training"

Connected through hyperedge HE-3 (induced by source paper "generating multiple diverse hypotheses for human 3d pose consistent with 2d joint detections"):
  with members:
    - "convolutional pose machines"
    - "stacked hourglass networks for human pose estimation"
    - "mocap guided data augmentation for 3d pose estimation in the wild"
    - "keep it smpl automatic estimation of 3d human pose and shape from a single image"
    - "synthesizing training images for boosting human 3d pose estimation"
    - "structured feature learning for pose estimation"
    - "human pose estimation via convolutional part heatmap regression"
    - "crf cnn modeling structured information in human pose estimation"

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)