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: "learning to adapt for stereo"
  Abstract: Real world applications of stereo depth estimation require models that are robust to dynamic variations in the environment. Even though deep learning based stereo methods are successful, they often fail to generalize to unseen variations in the environment, making them less suitable for practical applications such as autonomous driving. In this work, we introduce a "learning-to-adapt" framework that enables deep stereo methods to continuously adapt to new target domains in an unsupervised manner. Specifically, our approach incorporates the adaptation procedure into the learning objective to obtain a base set of parameters that are better suited for unsupervised online adaptation. To further improve the quality of the adaptation, we learn a confidence measure that effectively masks the errors introduced during the unsupervised adaptation. We evaluate our method on synthetic and real-world stereo datasets and our experiments evidence that learning-to-adapt is, indeed beneficial for online adaptation on vastly different domains.

Connected through hyperedge HE-1 (induced by source paper "domain invariant stereo matching networks"):
  with members:
    - "edgestereo an effective multi task learning network for stereo matching and edge detection"
    - "guided stereo matching"
    - "group wise correlation stereo network"
    - "semantic image synthesis with spatially adaptive normalization"
    - "real time self adaptive deep stereo"
    - "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-2 (induced by source paper "learning end to end scene flow by distilling single tasks knowledge"):
  with members:
    - "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"
    - "real time self adaptive deep stereo"

Connected through hyperedge HE-3 (induced by source paper "joint demosaicing and denoising by overfitting of bursts of raw images"):
  with members:
    - "handheld multi frame super resolution"
    - "noise2self blind denoising by self supervision"
    - "real time self adaptive deep stereo"
    - "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-4 (induced by source paper "joint demosaicking and denoising by fine tuning of bursts of raw images"):
  with members:
    - "noise2self blind denoising by self supervision"
    - "real time self adaptive deep stereo"
    - "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)