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 graph matching via affinity optimization with graduated consistency regularization"
  Abstract: This paper addresses the problem of matching common node correspondences among multiple graphs referring to an identical or related structure. This multi-graph matching problem involves two correlated components: i) the local pairwise matching affinity across pairs of graphs; ii) the global matching consistency that measures the uniqueness of the pairwise matchings by different composition orders. Previous studies typically either enforce the matching consistency constraints in the beginning of an iterative optimization, which may propagate matching error both over iterations and across graph pairs; or separate affinity optimization and consistency enforcement into two steps. This paper is motivated by the observation that matching consistency can serve as a regularizer in the affinity objective function especially when the function is biased due to noises or inappropriate modeling. We propose composition-based multi-graph matching methods to incorporate the two aspects by optimizing the affinity score, meanwhile gradually infusing the consistency. We also propose two mechanisms to elicit the common inliers against outliers. Compelling results on synthetic and real images show the competency of our algorithms.

Connected through hyperedge HE-1 (induced by source paper "adapnet adaptability decomposing encoder decoder network for weakly supervised action recognition and localization"):
  with members:
    - "end to end active object tracking and its real world deployment via reinforcement learning"
    - "exploiting feature and class relationships in video categorization with regularized deep neural networks"
    - "rethinking the faster r cnn architecture for temporal action localization"
    - "bsn boundary sensitive network for temporal action proposal generation"
    - "modeling multimodal clues in a hybrid deep learning framework for video classification"
    - "single shot temporal action detection"
    - "temporal action detection with structured segment networks"
    - "untrimmednets for weakly supervised action recognition and detection"

Connected through hyperedge HE-2 (induced by source paper "consistent cross view matching for unsupervised person re identification"):
  with members:
    - "unsupervised person re identification by soft multilabel learning"
    - "invariance matters exemplar memory for domain adaptive person re identification"
    - "multi level factorisation net for person re identification"
    - "learning discriminative features with multiple granularities for person re identification"
    - "deep association learning for unsupervised video person re identification"
    - "transferable joint attribute identity deep learning for unsupervised person re identification"
    - "dual attention matching network for context aware feature sequence based person re identification"
    - "part aligned bilinear representations for person re identification"

Connected through hyperedge HE-3 (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"
    - "multi image semantic matching by mining consistent features"

Connected through hyperedge HE-4 (induced by source paper "synchronisation of partial multi matchings via non negative factorisations"):
  with members:
    - "ds a flexible scalable and provably tight relaxation for matching problems"
    - "distributable consistent multi object matching"
    - "a constrained clustering based approach for matching a collection of feature sets"
    - "spectral synchronization of multiple views in se 3"
    - "efficient and non convex coordinate descent for symmetric nonnegative matrix factorization"
    - "a dual ascent framework for lagrangean decomposition of combinatorial problems"
    - "alternating direction graph matching"
    - "a solution for multi alignment by transformation synchronisation"

Connected through hyperedge HE-5 (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"
    - "multi image semantic matching by mining consistent features"
    - "distributable consistent multi object matching"
    - "a constrained clustering based approach for matching a collection of feature sets"
    - "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-6 (induced by source paper "neural graph matching network learning lawler s quadratic assignment problem with extension to hypergraph and multiple graph matching"):
  with members:
    - "learning combinatorial embedding networks for deep graph matching"
    - "exploiting edge features in graph neural networks"
    - "semi supervised classification with graph convolutional networks"
    - "very deep convolutional networks for large scale image recognition"
    - "near optimal joint object matching via convex relaxation"
    - "algorithmic differentiation of linear algebra functions with application in optimum experimental design extended version"
    - "learning graph matching"

Connected through hyperedge HE-7 (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"
    - "multi image semantic matching by mining consistent features"
    - "the projected power method an efficient algorithm for joint alignment from pairwise differences"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "multi image semantic matching by mining consistent features"):
  with members:
    - "anchornet a weakly supervised network to learn geometry sensitive features for semantic matching"
    - "proposal flow semantic correspondences from object proposals"
    - "unsupervised learning of object landmarks by factorized spatial embeddings"
    - "lift learned invariant feature transform"
    - "warpnet weakly supervised matching for single view reconstruction"
    - "learning dense correspondence via 3d guided cycle consistency"
    - "learning covariant feature detectors"
    - "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)