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: "dgc net dense geometric correspondence network"
  Abstract: This paper addresses the challenge of dense pixel correspondence estimation between two images. This problem is closely related to optical flow estimation task where ConvNets (CNNs) have recently achieved significant progress. While optical flow methods produce very accurate results for the small pixel translation and limited appearance variation scenarios, they hardly deal with the strong geometric transformations that we consider in this work. In this paper, we propose a coarse-to-fine CNN-based framework that can leverage the advantages of optical flow approaches and extend them to the case of large transformations providing dense and subpixel accurate estimates. It is trained on synthetic transformations and demonstrates very good performance to unseen, realistic, data. Further, we apply our method to the problem of relative camera pose estimation and demonstrate that the model outperforms existing dense approaches.

Connected through hyperedge HE-1 (induced by source paper "scopeflow dynamic scene scoping for optical flow"):
  with members:
    - "a lightweight optical flow cnn revisiting data fidelity and regularization"
    - "iterative residual refinement for joint optical flow and occlusion estimation"
    - "ddflow learning optical flow with unlabeled data distillation"
    - "selflow self supervised learning of optical flow"
    - "what makes good synthetic training data for learning disparity and optical flow estimation"
    - "models matter so does training an empirical study of cnns for optical flow estimation"
    - "a fusion approach for multi frame optical flow estimation"
    - "proflow learning to predict optical flow"

Connected through hyperedge HE-2 (induced by source paper "geometric image correspondence verification by dense pixel matching"):
  with members:
    - "d2 net a trainable cnn for joint detection and description of local features"
    - "neighbourhood consensus networks"
    - "from coarse to fine robust hierarchical localization at large scale"
    - "inloc indoor visual localization with dense matching and view synthesis"
    - "convolutional neural network architecture for geometric matching"
    - "pwc net cnns for optical flow using pyramid warping and cost volume"
    - "image patch matching using convolutional descriptors with euclidean distance"
    - "fine tuning cnn image retrieval with no human annotation"

Connected through hyperedge HE-3 (induced by source paper "an analysis of object embeddings for image retrieval"):
  with members:
    - "learning rich features for image manipulation detection"
    - "do better imagenet models transfer better"
    - "deep learning for generic object detection a survey"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"
    - "the ps battles dataset an image collection for image manipulation detection"
    - "sniper efficient multi scale training"
    - "deep metric learning with hierarchical triplet loss"
    - "detect to retrieve efficient regional aggregation for image search"

Connected through hyperedge HE-4 (induced by source paper "glu net global local universal network for dense flow and correspondences"):
  with members:
    - "geometric image correspondence verification by dense pixel matching"
    - "dynamic context correspondence network for semantic alignment"
    - "an empirical evaluation study on the training of sdc features for dense pixel matching"
    - "a lightweight optical flow cnn revisiting data fidelity and regularization"
    - "glampoints greedily learned accurate match points"
    - "semantic attribute matching networks"
    - "what makes good synthetic training data for learning disparity and optical flow estimation"
    - "lf net learning local features from images"

Connected through hyperedge HE-5 (induced by source paper "kneel knee anatomical landmark localization using hourglass networks"):
  with members:
    - "geometric image correspondence verification by dense pixel matching"
    - "facial landmark detection a literature survey"
    - "wing loss for robust facial landmark localisation with convolutional neural networks"
    - "automatic knee osteoarthritis diagnosis from plain radiographs a deep learning based approach"
    - "a general and adaptive robust loss function"
    - "mixup beyond empirical risk minimization"
    - "automatic detection of knee joints and quantification of knee osteoarthritis severity using convolutional neural networks"
    - "a novel method for automatic localization of joint area on knee plain radiographs"

Connected through hyperedge HE-6 (induced by source paper "superncn neighbourhood consensus network for robust outdoor scenes matching"):
  with members:
    - "neighbourhood consensus networks"
    - "scone siamese constellation embedding descriptor for image matching"
    - "benchmarking 6dof urban visual localization in changing conditions"
    - "superpoint self supervised interest point detection and description"
    - "optical flow estimation using a spatial pyramid network"
    - "real time single image and video super resolution using an efficient sub pixel convolutional neural network"
    - "deepmatching hierarchical deformable dense matching"

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)