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: "all graphs lead to rome learning geometric and cycle consistent representations with graph convolutional networks"
  Abstract: Image feature matching is a fundamental part of many geometric computer vision applications, and using multiple images can improve performance. In this work, we formulate multi-image matching as a graph embedding problem then use a Graph Convolutional Network to learn an appropriate embedding function for aligning image features. We use cycle consistency to train our network in an unsupervised fashion, since ground truth correspondence is difficult or expensive to aquire. In addition, geometric consistency losses can be added at training time, even if the information is not available in the test set, unlike previous approaches that optimize cycle consistency directly. To the best of our knowledge, no other works have used learning for multi-image feature matching. Our experiments show that our method is competitive with other optimization based approaches.

Connected through hyperedge HE-1 (induced by source paper "3dregnet a deep neural network for 3d point registration"):
  with members:
    - "learning transformation synchronization"
    - "ppfnet global context aware local features for robust 3d point matching"
    - "regnet learning the optimization of direct image to image pose registration"
    - "deepmapping unsupervised map estimation from multiple point clouds"
    - "3dfeat net weakly supervised local 3d features for point cloud registration"
    - "eigendecomposition free training of deep networks with zero eigenvalue based losses"
    - "automatic cnn based detection of cardiac mr motion artefacts using k space data augmentation and curriculum learning"
    - "teacher student curriculum learning"

Connected through hyperedge HE-2 (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"
    - "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"
    - "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"

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)