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: "multi path feedback recurrent neural network for scene parsing"
  Abstract: In this paper, we consider the scene parsing problem and propose a novel Multi-Path Feedback recurrent neural network (MPF-RNN) for parsing scene images. MPF-RNN can enhance the capability of RNNs in modeling long-range context information at multiple levels and better distinguish pixels that are easy to confuse. Different from feedforward CNNs and RNNs with only single feedback, MPF-RNN propagates the contextual features learned at top layer through \textit{multiple} weighted recurrent connections to learn bottom features. For better training MPF-RNN, we propose a new strategy that considers accumulative loss at multiple recurrent steps to improve performance of the MPF-RNN on parsing small objects. With these two novel components, MPF-RNN has achieved significant improvement over strong baselines (VGG16 and Res101) on five challenging scene parsing benchmarks, including traditional SiftFlow, Barcelona, CamVid, Stanford Background as well as the recently released large-scale ADE20K.

Connected through hyperedge HE-1 (induced by source paper "accessing higher level representations in sequential transformers with feedback memory"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "reducing transformer depth on demand with structured dropout"
    - "augmenting self attention with persistent memory"
    - "compressive transformers for long range sequence modelling"
    - "pre trained language model representations for language generation"
    - "r transformer recurrent neural network enhanced transformer"
    - "transformer xl attentive language models beyond a fixed length context"
    - "universal transformers"

Connected through hyperedge HE-2 (induced by source paper "distributed iterative gating networks for semantic segmentation"):
  with members:
    - "gated multiple feedback network for image super resolution"
    - "recurrent iterative gating networks for semantic segmentation"
    - "large kernel matters improve semantic segmentation by global convolutional network"
    - "object region mining with adversarial erasing a simple classification to semantic segmentation approach"
    - "recurrent scene parsing with perspective understanding in the loop"
    - "convolutional networks with adaptive inference graphs"
    - "iterative deep convolutional encoder decoder network for medical image segmentation"
    - "not all pixels are equal difficulty aware semantic segmentation via deep layer cascade"

Connected through hyperedge HE-3 (induced by source paper "gated multiple feedback network for image super resolution"):
  with members:
    - "feedback network for image super resolution"
    - "image super resolution using very deep residual channel attention networks"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "top down feedback for crowd counting convolutional neural network"
    - "non local recurrent network for image restoration"
    - "residual dense network for image super resolution"
    - "image super resolution via dual state recurrent networks"
    - "sketch based manga retrieval using manga109 dataset"

Connected through hyperedge HE-4 (induced by source paper "low latency video semantic segmentation"):
  with members:
    - "semantic video cnns through representation warping"
    - "enet a deep neural network architecture for real time semantic segmentation"
    - "stfcn spatio temporal fcn for semantic video segmentation"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "semantic video segmentation by gated recurrent flow propagation"
    - "learning video object segmentation from static images"
    - "video scene parsing with predictive feature learning"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"

Connected through hyperedge HE-5 (induced by source paper "generative scatternet hybrid deep learning g shdl network with structural priors for semantic image segmentation"):
  with members:
    - "texture and structure incorporated scatternet hybrid deep learning network ts shdl for brain matter segmentation"
    - "discriminative training of deep fully connected continuous crfs with task specific loss"
    - "dual tree wavelet scattering network with parametric log transformation for object classification"
    - "scatternet hybrid deep learning shdl network for object classification"
    - "a review on deep learning techniques applied to semantic segmentation"
    - "multi resolution dual tree wavelet scattering network for signal classification"
    - "pcanet a simple deep learning baseline for image classification"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-6 (induced by source paper "video scene parsing with predictive feature learning"):
  with members:
    - "dense recurrent neural networks for scene labeling"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "laplacian reconstruction and refinement for semantic segmentation"
    - "pixel level encoding and depth layering for instance level semantic labeling"
    - "approximate policy iteration for budgeted semantic video segmentation"
    - "optical flow with semantic segmentation and localized layers"
    - "high performance semantic segmentation using very deep fully convolutional networks"
    - "joint optical flow and temporally consistent semantic segmentation"

Connected through hyperedge HE-7 (induced by source paper "scene labeling using gated recurrent units with explicit long range conditioning"):
  with members:
    - "dense recurrent neural networks for scene labeling"
    - "semantic object parsing with graph lstm"
    - "lstm cf unifying context modeling and fusion with lstms for rgb d scene labeling"
    - "multi level contextual rnns with attention model for scene labeling"
    - "learning common and specific features for rgb d semantic segmentation with deconvolutional networks"
    - "combining the best of convolutional layers and recurrent layers a hybrid network for semantic segmentation"
    - "pixel recurrent neural networks"
    - "semantic image segmentation via deep parsing network"

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)