human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "higher order potentials in end to end trainable conditional random fields"):
  Abstract: We tackle the problem of semantic segmentation using deep learning techniques. Most semantic segmentation systems include a Conditional Random Field (CRF) model to produce a structured output that is consistent with visual features of the image. With recent advances in deep learning, it is becoming increasingly common to perform CRF inference within a deep neural network to facilitate joint learning of the CRF with a pixel-wise Convolutional Neural Network (CNN) classifier. #R##N#While basic CRFs use only unary and pairwise potentials, it has been shown that the addition of higher order potentials defined on cliques with more than two nodes can result in a better segmentation outcome. In this paper, we show that two types of higher order potential, namely, object detection based potentials and superpixel based potentials, can be included in a CRF embedded within a deep network. We design these higher order potentials to allow inference with the efficient and differentiable mean-field algorithm, making it possible to implement our CRF model as a stack of layers in a deep network. As a result, all parameters of our richer CRF model can be jointly learned with a CNN classifier during the end-to-end training of the entire network. We find significant improvement in the results with the introduction of these trainable higher order potentials.

Includes members:
  Member "semantic image segmentation via deep parsing network":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "attention guided chained context aggregation for semantic segmentation")
  Member "deep structured models for group activity recognition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "captioning near future activity sequences")
  Member "fast r cnn":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis")
  Member "boxsup exploiting bounding boxes to supervise convolutional networks for semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning to adapt class specific features across domains for semantic segmentation")
  Member "faster r cnn towards real time object detection with region proposal networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "relatext exploiting visual relationships for arbitrary shaped scene text detection with graph convolutional networks")
  Member "efficient piecewise training of deep structured models for semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "attention guided chained context aggregation for semantic segmentation")
  Member "convolutional feature masking for joint object and stuff segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "adaptive binarization based on fuzzy integrals")
  Member "joint training of a convolutional network and a graphical model for human pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "object detection in optical remote sensing images a survey and a new benchmark")

Question: Please tell me which class the source paper 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)