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: "combining the best of graphical models and convnets for semantic segmentation"
  Abstract: We present a two-module approach to semantic segmentation that incorporates Convolutional Networks (CNNs) and Graphical Models. Graphical models are used to generate a small (5-30) set of diverse segmentations proposals, such that this set has high recall. Since the number of required proposals is so low, we can extract fairly complex features to rank them. Our complex feature of choice is a novel CNN called SegNet, which directly outputs a (coarse) semantic segmentation. Importantly, SegNet is specifically trained to optimize the corpus-level PASCAL IOU loss function. To the best of our knowledge, this is the first CNN specifically designed for semantic segmentation. This two-module approach achieves $52.5\%$ on the PASCAL 2012 segmentation challenge.

Connected through hyperedge HE-1 (induced by source paper "bottom up top down cues for weakly supervised semantic segmentation"):
  with members:
    - "deeply supervised salient object detection with short connections"
    - "stc a simple to complex framework for weakly supervised semantic segmentation"
    - "seed expand and constrain three principles for weakly supervised image segmentation"
    - "scribblesup scribble supervised convolutional networks for semantic segmentation"
    - "fast exact and multi scale inference for semantic image segmentation with deep gaussian crfs"
    - "weakly and semi supervised learning of a dcnn for semantic image segmentation"
    - "constrained convolutional neural networks for weakly supervised segmentation"
    - "learning deep features for discriminative localization"

Connected through hyperedge HE-2 (induced by source paper "learning depth from single monocular images using deep convolutional neural fields"):
  with members:
    - "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"
    - "return of the devil in the details delving deep into convolutional nets"
    - "depth transfer depth extraction from video using non parametric sampling"
    - "depth map prediction from a single image using a multi scale deep network"
    - "category specific object reconstruction from a single image"
    - "cnn features off the shelf an astounding baseline for recognition"

Connected through hyperedge HE-3 (induced by source paper "semantic image segmentation with deep convolutional nets and fully connected crfs"):
  with members:
    - "weakly and semi supervised learning of a dcnn for semantic image segmentation"
    - "conditional random fields as recurrent neural networks"
    - "convolutional feature masking for joint object and stuff segmentation"
    - "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"

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)