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: "real time self adaptive deep stereo"
  Abstract: Deep convolutional neural networks trained end-to-end are the undisputed state-of-the-art methods to regress dense disparity maps directly from stereo pairs. However, such methods suffer from notable accuracy drops when exposed to scenarios significantly different from those seen in the training phase (e.g.real vs synthetic images, indoor vs outdoor, etc). As it is unlikely to be able to gather enough samples to achieve effective training/ tuning in any target domain, we propose to perform unsupervised and continuous online adaptation of a deep stereo network in order to preserve its accuracy independently of the sensed environment. However, such a strategy can be extremely demanding regarding computational resources and thus not enabling real-time performance. Therefore, we address this side effect by introducing a new lightweight, yet effective, deep stereo architecture Modularly ADaptive Network (MADNet) and by developing Modular ADaptation (MAD), an algorithm to train independently only sub-portions of our model. By deploying MADNet together with MAD we propose the first ever realtime self-adaptive deep stereo system.

Connected through hyperedge HE-1 (induced by source paper "learning monocular depth estimation infusing traditional stereo knowledge"):
  with members:
    - "refine and distill exploiting cycle inconsistency and knowledge distillation for unsupervised monocular depth estimation"
    - "guided stereo matching"
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "segstereo exploiting semantic information for disparity estimation"
    - "deep ordinal regression network for monocular depth estimation"
    - "df net unsupervised joint learning of depth and flow using cross task consistency"
    - "single view stereo matching"

Connected through hyperedge HE-2 (induced by source paper "domain invariant stereo matching networks"):
  with members:
    - "edgestereo an effective multi task learning network for stereo matching and edge detection"
    - "learning to adapt for stereo"
    - "guided stereo matching"
    - "group wise correlation stereo network"
    - "semantic image synthesis with spatially adaptive normalization"
    - "segstereo exploiting semantic information for disparity estimation"
    - "feature denoising for improving adversarial robustness"
    - "zoom and learn generalizing deep stereo matching to novel domains"

Connected through hyperedge HE-3 (induced by source paper "sterefo efficient image refocusing with stereo vision"):
  with members:
    - "synthetic depth of field with a single camera mobile phone"
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "on the importance of stereo for accurate depth estimation an efficient semi supervised deep neural network approach"
    - "deeplens shallow depth of field from a single image"
    - "the 2018 pirm challenge on perceptual image super resolution"
    - "df net unsupervised joint learning of depth and flow using cross task consistency"
    - "learning monocular visual odometry with dense 3d mapping from dense 3d flow"
    - "the perception distortion tradeoff"

Connected through hyperedge HE-4 (induced by source paper "online adaptation through meta learning for stereo depth estimation"):
  with members:
    - "model adaptation with synthetic and real data for semantic dense foggy scene understanding"
    - "unsupervised adversarial depth estimation using cycled generative networks"
    - "adadepth unsupervised content congruent adaptation for depth estimation"
    - "deep ordinal regression network for monocular depth estimation"
    - "meta tracker fast and robust online adaptation for visual object trackers"
    - "boosting domain adaptation by discovering latent domains"
    - "structured attention guided convolutional neural fields for monocular depth estimation"
    - "dark model adaptation semantic image segmentation from daytime to nighttime"

Connected through hyperedge HE-5 (induced by source paper "learning end to end scene flow by distilling single tasks knowledge"):
  with members:
    - "learning to adapt for stereo"
    - "sense a shared encoder network for scene flow estimation"
    - "guided stereo matching"
    - "ga net guided aggregation net for end to end stereo matching"
    - "pwoc 3d deep occlusion aware end to end scene flow estimation"
    - "ddflow learning optical flow with unlabeled data distillation"
    - "real time semantic stereo matching"
    - "deep rigid instance scene flow"

Connected through hyperedge HE-6 (induced by source paper "learning to adapt for stereo"):
  with members:
    - "zoom and learn generalizing deep stereo matching to novel domains"
    - "learning to adapt in dynamic real world environments through meta reinforcement learning"
    - "open world stereo video matching with deep rnn"
    - "learning to reweight examples for robust deep learning"
    - "activestereonet end to end self supervised learning for active stereo systems"
    - "left right comparative recurrent model for stereo matching"
    - "pyramid stereo matching network"
    - "learning monocular depth estimation with unsupervised trinocular assumptions"

Connected through hyperedge HE-7 (induced by source paper "joint demosaicing and denoising by overfitting of bursts of raw images"):
  with members:
    - "handheld multi frame super resolution"
    - "learning to adapt for stereo"
    - "noise2self blind denoising by self supervision"
    - "toward convolutional blind denoising of real photographs"
    - "noise2void learning denoising from single noisy images"
    - "noise2noise learning image restoration without clean data"
    - "learning to see in the dark"
    - "model blind video denoising via frame to frame training"

Connected through hyperedge HE-8 (induced by source paper "joint demosaicking and denoising by fine tuning of bursts of raw images"):
  with members:
    - "learning to adapt for stereo"
    - "noise2self blind denoising by self supervision"
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "toward convolutional blind denoising of real photographs"
    - "noise2void learning denoising from single noisy images"
    - "deep image demosaicking using a cascade of convolutional residual denoising networks"
    - "noise2noise learning image restoration without clean data"
    - "learning to see in the dark"

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)