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: "multi image semantic matching by mining consistent features"
  Abstract: This work proposes a multi-image matching method to estimate semantic correspondences across multiple images. In contrast to the previous methods that optimize all pairwise correspondences, the proposed method identifies and matches only a sparse set of reliable features in the image collection. In this way, the proposed method is able to prune nonrepeatable features and also highly scalable to handle thousands of images. We additionally propose a low-rank constraint to ensure the geometric consistency of feature correspondences over the whole image collection. Besides the competitive performance on multi-graph matching and semantic flow benchmarks, we also demonstrate the applicability of the proposed method to reconstruct object-class models and discover object-class landmarks from images without any annotation.

Connected through hyperedge HE-1 (induced by source paper "probabilistic permutation synchronization using the riemannian structure of the birkhoff polytope"):
  with members:
    - "synchronisation of partial multi matchings via non negative factorisations"
    - "ppfnet global context aware local features for robust 3d point matching"
    - "bayesian pose graph optimization via bingham distributions and tempered geodesic mcmc"
    - "averaging stochastic gradient descent on riemannian manifolds"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "cad priors for accurate and flexible instance reconstruction"
    - "consistent multiple graph matching with multi layer random walks synchronization"
    - "distributable consistent multi object matching"

Connected through hyperedge HE-2 (induced by source paper "all graphs lead to rome learning geometric and cycle consistent representations with graph convolutional networks"):
  with members:
    - "group normalization"
    - "relational inductive biases deep learning and graph networks"
    - "mimo graph filters for convolutional neural networks"
    - "convolutional neural networks via node varying graph filters"
    - "geometric deep learning going beyond euclidean data"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "learning to find good correspondences"
    - "lift learned invariant feature transform"

Connected through hyperedge HE-3 (induced by source paper "clear a consistent lifting embedding and alignment rectification algorithm for multi agent data association"):
  with members:
    - "synchronisation of partial multi matchings via non negative factorisations"
    - "all graphs lead to rome learning geometric and cycle consistent representations with graph convolutional networks"
    - "distributable consistent multi object matching"
    - "a constrained clustering based approach for matching a collection of feature sets"
    - "multi graph matching via affinity optimization with graduated consistency regularization"
    - "netvlad cnn architecture for weakly supervised place recognition"
    - "multi image matching via fast alternating minimization"
    - "near optimal joint object matching via convex relaxation"

Connected through hyperedge HE-4 (induced by source paper "distributed and consistent multi image feature matching via quickmatch"):
  with members:
    - "ps dbscan an efficient parallel dbscan algorithm based on platform of ai pai"
    - "billion scale similarity search with gpus"
    - "anchornet a weakly supervised network to learn geometry sensitive features for semantic matching"
    - "accelerated nearest neighbor search with quick adc"
    - "distributable consistent multi object matching"
    - "multi image matching via fast alternating minimization"
    - "near optimal joint object matching via convex relaxation"
    - "hypercolumns for object segmentation and fine grained localization"

Connected through hyperedge HE-5 (induced by source paper "discovery of latent 3d keypoints via end to end geometric reasoning"):
  with members:
    - "unsupervised geometry aware representation for 3d human pose estimation"
    - "unsupervised learning of object landmarks through conditional image generation"
    - "unsupervised discovery of object landmarks as structural representations"
    - "training deep networks with synthetic data bridging the reality gap by domain randomization"
    - "densepose dense human pose estimation in the wild"
    - "unsupervised learning of object frames by dense equivariant image labelling"
    - "learning feature pyramids for human pose estimation"
    - "domain randomization for transferring deep neural networks from simulation to the real world"

Connected through hyperedge HE-6 (induced by source paper "higher order projected power iterations for scalable multi matching"):
  with members:
    - "product manifold filter non rigid shape correspondence via kernel density estimation in the product space"
    - "ds a flexible scalable and provably tight relaxation for matching problems"
    - "consistent multiple graph matching with multi layer random walks synchronization"
    - "the projected power method an efficient algorithm for joint alignment from pairwise differences"
    - "multi graph matching via affinity optimization with graduated consistency regularization"
    - "a flexible tensor block coordinate ascent scheme for hypergraph matching"
    - "multi image matching via fast alternating minimization"
    - "near optimal joint object matching via convex relaxation"

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)