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: "open world stereo video matching with deep rnn"
  Abstract: Deep Learning based stereo matching methods have shown great successes and achieved top scores across different benchmarks. However, like most data-driven methods, existing deep stereo matching networks suffer from some well-known drawbacks such as requiring large amount of labeled training data, and that their performances are fundamentally limited by the generalization ability. In this paper, we propose a novel Recurrent Neural Network (RNN) that takes a continuous (possibly previously unseen) stereo video as input, and directly predicts a depth-map at each frame without a pre-training process, and without the need of ground-truth depth-maps as supervision. Thanks to the recurrent nature (provided by two convolutional-LSTM blocks), our network is able to memorize and learn from its past experiences, and modify its inner parameters (network weights) to adapt to previously unseen or unfamiliar environments. This suggests a remarkable generalization ability of the net, making it applicable in an {\em open world} setting. Our method works robustly with changes in scene content, image statistics, and lighting and season conditions {\em etc}. By extensive experiments, we demonstrate that the proposed method seamlessly adapts between different scenarios. Equally important, in terms of the stereo matching accuracy, it outperforms state-of-the-art deep stereo approaches on standard benchmark datasets such as KITTI and Middlebury stereo.

Connected through hyperedge HE-1 (induced by source paper "group wise correlation stereo network"):
  with members:
    - "feature intertwiner for object detection"
    - "practical deep stereo pds toward applications friendly deep stereo matching"
    - "group normalization"
    - "segstereo exploiting semantic information for disparity estimation"
    - "deep stereo matching with explicit cost aggregation sub architecture"
    - "learning monocular depth by distilling cross domain stereo networks"
    - "stereonet guided hierarchical refinement for real time edge aware depth prediction"
    - "edgestereo a context integrated residual pyramid network for stereo matching"

Connected through hyperedge HE-2 (induced by source paper "learning to adapt for stereo"):
  with members:
    - "real time self adaptive deep stereo"
    - "zoom and learn generalizing deep stereo matching to novel domains"
    - "learning to adapt in dynamic real world environments through meta reinforcement learning"
    - "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-3 (induced by source paper "mvs 2 deep unsupervised multi view stereo with multi view symmetry"):
  with members:
    - "dpsnet end to end deep plane sweep stereo"
    - "recurrent mvsnet for high resolution multi view stereo depth inference"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "df net unsupervised joint learning of depth and flow using cross task consistency"
    - "deepmvs learning multi view stereopsis"
    - "mvsnet depth inference for unstructured multi view stereo"
    - "mvdepthnet real time multiview depth estimation neural network"
    - "learning a multi view stereo machine"

Connected through hyperedge HE-4 (induced by source paper "noise aware unsupervised deep lidar stereo fusion"):
  with members:
    - "sparse and dense data with cnns depth completion and semantic segmentation"
    - "activestereonet end to end self supervised learning for active stereo systems"
    - "pyramid stereo matching network"
    - "deep convolutional compressed sensing for lidar depth completion"
    - "self supervised learning for stereo matching with self improving ability"
    - "sparsity invariant cnns"
    - "a large dataset to train convolutional networks for disparity optical flow and scene flow estimation"
    - "deeper depth prediction with fully convolutional residual networks"

Connected through hyperedge HE-5 (induced by source paper "semi dense stereo matching using dual cnns"):
  with members:
    - "left right comparative recurrent model for stereo matching"
    - "pyramid stereo matching network"
    - "cbmv a coalesced bidirectional matching volume for disparity estimation"
    - "intel realsense stereoscopic depth cameras"
    - "look wider to match image patches with convolutional neural networks"
    - "cascade residual learning a two stage convolutional neural network for stereo matching"
    - "stereo matching by training a convolutional neural network to compare image patches"
    - "learning to compare image patches via convolutional neural networks"

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)