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: "parsing semantic parts of cars using graphical models and segment appearance consistency"
  Abstract: This paper addresses the problem of semantic part parsing (segmentation) of cars, i.e.assigning every pixel within the car to one of the parts (e.g.body, window, lights, license plates and wheels). We formulate this as a landmark identification problem, where a set of landmarks specifies the boundaries of the parts. A novel mixture of graphical models is proposed, which dynamically couples the landmarks to a hierarchy of segments. When modeling pairwise relation between landmarks, this coupling enables our model to exploit the local image contents in addition to spatial deformation, an aspect that most existing graphical models ignore. In particular, our model enforces appearance consistency between segments within the same part. Parsing the car, including finding the optimal coupling between landmarks and segments in the hierarchy, is performed by dynamic programming. We evaluate our method on a subset of PASCAL VOC 2010 car images and on the car subset of 3D Object Category dataset (CAR3D). We show good results and, in particular, quantify the effectiveness of using the segment appearance consistency in terms of accuracy of part localization and segmentation.

Connected through hyperedge HE-1 (induced by source paper "look into person joint body parsing pose estimation network and a new benchmark"):
  with members:
    - "active self paced learning for cost effective and progressive face identification"
    - "multi context attention for human pose estimation"
    - "convolutional pose machines"
    - "stacked hourglass networks for human pose estimation"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "human pose estimation via convolutional part heatmap regression"
    - "proposal free network for instance level object segmentation"
    - "semantic object parsing with local global long short term memory"

Connected through hyperedge HE-2 (induced by source paper "look into person self supervised structure sensitive learning and a new benchmark for human parsing"):
  with members:
    - "proposal free network for instance level object segmentation"
    - "semantic object parsing with local global long short term memory"
    - "joint object and part segmentation using deep learned potentials"
    - "conditional random fields as recurrent neural networks"
    - "zoom better to see clearer human part segmentation with auto zoom net"
    - "attention to scale scale aware semantic image segmentation"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"
    - "matching cnn meets knn quasi parametric human parsing"

Connected through hyperedge HE-3 (induced by source paper "semantic object parsing with graph lstm"):
  with members:
    - "pixel recurrent neural networks"
    - "semantic image segmentation via deep parsing network"
    - "semantic object parsing with local global long short term memory"
    - "parallel multi dimensional lstm with application to fast biomedical volumetric image segmentation"
    - "joint object and part segmentation using deep learned potentials"
    - "conditional random fields as recurrent neural networks"
    - "zoom better to see clearer human part segmentation with auto zoom net"
    - "attention to scale scale aware semantic image segmentation"

Connected through hyperedge HE-4 (induced by source paper "semantic object parsing with local global long short term memory"):
  with members:
    - "semantic image segmentation via deep parsing network"
    - "proposal free network for instance level object segmentation"
    - "parallel multi dimensional lstm with application to fast biomedical volumetric image segmentation"
    - "joint object and part segmentation using deep learned potentials"
    - "conditional random fields as recurrent neural networks"
    - "show attend and tell neural image caption generation with visual attention"
    - "grid long short term memory"
    - "matching cnn meets knn quasi parametric human parsing"

Connected through hyperedge HE-5 (induced by source paper "joint object and part segmentation using deep learned potentials"):
  with members:
    - "segdeepm exploiting segmentation and context in deep neural networks for object detection"
    - "efficient piecewise training of deep structured models for semantic segmentation"
    - "very deep convolutional networks for large scale image recognition"
    - "hypercolumns for object segmentation and fine grained localization"
    - "part based r cnns for fine grained category detection"
    - "semantic image segmentation with deep convolutional nets and fully connected crfs"
    - "simultaneous detection and segmentation"
    - "semantic part segmentation using compositional model combining shape and appearance"

Connected through hyperedge HE-6 (induced by source paper "deep learning for semantic part segmentation with high level guidance"):
  with members:
    - "fully connected deep structured networks"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "joint training of a convolutional network and a graphical model for human pose estimation"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "untangling local and global deformations in deep convolutional networks for image classification and sliding window detection"
    - "hypercolumns for object segmentation and fine grained localization"
    - "semantic image segmentation with deep convolutional nets and fully connected crfs"

Connected through hyperedge HE-7 (induced by source paper "semantic part segmentation using compositional model combining shape and appearance"):
  with members:
    - "simultaneous detection and segmentation"
    - "detect what you can detecting and representing objects using holistic models and body parts"
    - "deeppose human pose estimation via deep neural 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)