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: "fully connected deep structured networks"
  Abstract: Convolutional neural networks with many layers have recently been shown to achieve excellent results on many high-level tasks such as image classification, object detection and more recently also semantic segmentation. Particularly for semantic segmentation, a two-stage procedure is often employed. Hereby, convolutional networks are trained to provide good local pixel-wise features for the second step being traditionally a more global graphical model. In this work we unify this two-stage process into a single joint training algorithm. We demonstrate our method on the semantic image segmentation task and show encouraging results on the challenging PASCAL VOC 2012 dataset.

Connected through hyperedge HE-1 (induced by source paper "gated scnn gated shape cnns for semantic segmentation"):
  with members:
    - "devil is in the edges learning semantic boundaries from noisy annotations"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "fast interactive object annotation with curve gcn"
    - "efficient interactive annotation of segmentation datasets with polygon rnn"
    - "adaptive affinity fields for semantic segmentation"
    - "free form image inpainting with gated convolution"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "tree structured kronecker convolutional network for semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "i bet you are wrong gambling adversarial networks for structured semantic segmentation"):
  with members:
    - "understanding deep learning techniques for image segmentation"
    - "adversarial structure matching loss for image segmentation"
    - "star shape prior in fully convolutional networks for skin lesion segmentation"
    - "anatomically constrained neural networks acnns application to cardiac image enhancement and segmentation"
    - "student beats the teacher deep neural networks for lateral ventricles segmentation in brain mr"
    - "efficient relaxations for dense crfs with sparse higher order potentials"
    - "segan adversarial network with multi scale l 1 loss for medical image segmentation"
    - "adversarial learning for semi supervised semantic segmentation"

Connected through hyperedge HE-3 (induced by source paper "learning to reconstruct 3d human pose and shape via model fitting in the loop"):
  with members:
    - "raynet learning volumetric 3d reconstruction with ray potentials"
    - "convolutional mesh regression for single image human shape reconstruction"
    - "expressive body capture 3d hands face and body from a single image"
    - "exploiting temporal context for 3d human pose estimation in the wild"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "learning to estimate 3d human pose and shape from a single color image"
    - "bodynet volumetric inference of 3d human body shapes"
    - "total capture a 3d deformation model for tracking faces hands and bodies"

Connected through hyperedge HE-4 (induced by source paper "from big to small multi scale local planar guidance for monocular depth estimation"):
  with members:
    - "enforcing geometric constraints of virtual normal for depth prediction"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "deep ordinal regression network for monocular depth estimation"
    - "learning depth from monocular videos using direct methods"
    - "multi scale continuous crfs as sequential deep networks for monocular depth estimation"
    - "semi supervised deep learning for monocular depth map prediction"
    - "the cityscapes dataset for semantic urban scene understanding"

Connected through hyperedge HE-5 (induced by source paper "frank wolfe network an interpretable deep structure for non sparse coding"):
  with members:
    - "learning simple thresholded features with sparse support recovery"
    - "theoretical linear convergence of unfolded ista and its practical weights and thresholds"
    - "ista net interpretable optimization inspired deep network for image compressive sensing"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "a unified optimization view on generalized matching pursuit and frank wolfe"
    - "maximal sparsity with deep networks"
    - "learning optimal nonlinearities for iterative thresholding algorithms"
    - "normalizing the normalizers comparing and extending network normalization schemes"

Connected through hyperedge HE-6 (induced by source paper "value of temporal dynamics information in driving scene segmentation"):
  with members:
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "youtube vos sequence to sequence video object segmentation"
    - "the 2018 davis challenge on video object segmentation"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "the 2017 davis challenge on video object segmentation"
    - "learning video object segmentation with visual memory"
    - "rethinking atrous convolution for semantic image segmentation"
    - "the cityscapes dataset for semantic urban scene understanding"

Connected through hyperedge HE-7 (induced by source paper "structured prediction using cgans with fusion discriminator"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "cgans with projection discriminator"
    - "spectral normalization for generative adversarial networks"
    - "unsupervised reverse domain adaptation for synthetic medical images via adversarial training"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "improved training of wasserstein gans"
    - "deep learning and conditional random fields based depth estimation and topographical reconstruction from conventional endoscopy"
    - "photographic image synthesis with cascaded refinement networks"

Connected through hyperedge HE-8 (induced by source paper "video relationship reasoning using gated spatio temporal energy graph"):
  with members:
    - "context dependent diffusion network for visual relationship detection"
    - "discovering order in unordered datasets generative markov networks"
    - "zoom net mining deep feature interactions for visual relationship recognition"
    - "videos as space time region graphs"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "non local neural networks"
    - "attention is all you need"
    - "neural motifs scene graph parsing with global context"

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)