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: "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.

Connected through hyperedge HE-1 (induced by source paper "simulating crf with cnn for cnn"):
  with members:
    - "deeply supervised salient object detection with short connections"
    - "normalized cut loss for weakly supervised cnn segmentation"
    - "end to end training of hybrid cnn crf models for stereo"
    - "fast exact and multi scale inference for semantic image segmentation with deep gaussian crfs"
    - "computing the stereo matching cost with a convolutional neural network"
    - "learning sparse high dimensional filters image filtering dense crfs and bilateral neural networks"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-2 (induced by source paper "understanding convolution for semantic segmentation"):
  with members:
    - "the cityscapes dataset for semantic urban scene understanding"
    - "laplacian reconstruction and refinement for semantic segmentation"
    - "semantic understanding of scenes through the ade20k dataset"
    - "pixel level encoding and depth layering for instance level semantic labeling"
    - "optical flow with semantic segmentation and localized layers"
    - "mixed context networks for semantic segmentation"
    - "high performance semantic segmentation using very deep fully convolutional networks"
    - "ssd single shot multibox detector"

Connected through hyperedge HE-3 (induced by source paper "deep occlusion reasoning for multi camera multi target detection"):
  with members:
    - "the wildtrack multi camera person dataset"
    - "deep multi camera people detection"
    - "how far are we from solving pedestrian detection"
    - "ssd single shot multibox detector"
    - "multi modal mean fields via cardinality based clamping"
    - "social scene understanding end to end multi person action localization and collective activity recognition"
    - "principled parallel mean field inference for discrete random fields"
    - "you only look once unified real time object detection"

Connected through hyperedge HE-4 (induced by source paper "multi modal mean fields via cardinality based clamping"):
  with members:
    - "joint optical flow and temporally consistent semantic segmentation"
    - "principled parallel mean field inference for discrete random fields"
    - "conditional random fields as recurrent neural networks"
    - "multi scale context aggregation by dilated convolutions"
    - "clamping improves trw and mean field approximations"
    - "deepmatching hierarchical deformable dense matching"
    - "mean field networks"
    - "a comparative study of modern inference techniques for structured discrete energy minimization problems"

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)