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: "learning combinatorial embedding networks for deep graph matching"
  Abstract: Graph matching refers to finding node correspondence between graphs, such that the corresponding node and edge's affinity can be maximized. In addition with its NP-completeness nature, another important challenge is effective modeling of the node-wise and structure-wise affinity across graphs and the resulting objective, to guide the matching procedure effectively finding the true matching against noises. To this end, this paper devises an end-to-end differentiable deep network pipeline to learn the affinity for graph matching. It involves a supervised permutation loss regarding with node correspondence to capture the combinatorial nature for graph matching. Meanwhile deep graph embedding models are adopted to parameterize both intra-graph and cross-graph affinity functions, instead of the traditional shallow and simple parametric forms e.g. a Gaussian kernel. The embedding can also effectively capture the higher-order structure beyond second-order edges. The permutation loss model is agnostic to the number of nodes, and the embedding model is shared among nodes such that the network allows for varying numbers of nodes in graphs for training and inference. Moreover, our network is class-agnostic with some generalization capability across different categories. All these features are welcomed for real-world applications. Experiments show its superiority against state-of-the-art graph matching learning methods.

Connected through hyperedge HE-1 (induced by source paper "deep graph matching consensus"):
  with members:
    - "simgnn a neural network approach to fast graph similarity computation"
    - "cross lingual knowledge graph alignment via graph matching neural network"
    - "computing optimal assignments in linear time for approximate graph matching"
    - "scalable gromov wasserstein learning for graph partitioning and matching"
    - "deep adversarial network alignment"
    - "graph neural networks for user identity linkage"
    - "kergm kernelized graph matching"
    - "gromov wasserstein learning for graph matching and node embedding"

Connected through hyperedge HE-2 (induced by source paper "high order information matters learning relation and topology for occluded person re identification"):
  with members:
    - "deep high resolution representation learning for human pose estimation"
    - "a novel teacher student learning framework for occluded person re identification"
    - "stnreid deep convolutional networks with pairwise spatial transformer networks for partial person re identification"
    - "bag of tricks and a strong baseline for deep person re identification"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "maskreid a mask based deep ranking neural network for person re identification"
    - "scpnet spatial channel parallelism network for joint holistic and partial person re identification"
    - "horizontal pyramid matching for person re identification"

Connected through hyperedge HE-3 (induced by source paper "fast detection of maximum common subgraph via deep q learning"):
  with members:
    - "simgnn a neural network approach to fast graph similarity computation"
    - "learning to route in similarity graphs"
    - "a unifying framework for spectrum preserving graph sparsification and coarsening"
    - "scalable gromov wasserstein learning for graph partitioning and matching"
    - "gromov wasserstein learning for graph matching and node embedding"
    - "a learning based branch and bound for maximum common subgraph problems"
    - "fast graph representation learning with pytorch geometric"
    - "graph matching networks for learning the similarity of graph structured objects"

Connected through hyperedge HE-4 (induced by source paper "deep graph similarity learning a survey"):
  with members:
    - "simgnn a neural network approach to fast graph similarity computation"
    - "deep graph matching consensus"
    - "dynamic graph convolutional networks"
    - "glmnet graph learning matching networks for feature matching"
    - "from community to role based graph embeddings"
    - "community preserving graph convolutions for structural and functional joint embedding of brain networks"
    - "generalizing from a few examples a survey on few shot learning"
    - "rethinking kernel methods for node representation learning on graphs"

Connected through hyperedge HE-5 (induced by source paper "inspherenet a concise representation and classification method for 3d object"):
  with members:
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "learning 3d shape completion under weak supervision"
    - "dynamic graph cnn for learning on point clouds"
    - "joint cuts and matching of partitions in one graph"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "pointnet deep learning on point sets for 3d classification and segmentation"
    - "multi view convolutional neural networks for 3d shape recognition"
    - "3d shapenets a deep representation for volumetric shapes"

Connected through hyperedge HE-6 (induced by source paper "glmnet graph learning matching networks for feature matching"):
  with members:
    - "symmetric graph convolutional autoencoder for unsupervised graph representation learning"
    - "graph matching networks for learning the similarity of graph structured objects"
    - "a comprehensive survey on graph neural networks"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "semi supervised classification with graph convolutional networks"
    - "very deep convolutional networks for large scale image recognition"
    - "spectral networks and locally connected networks on graphs"

Connected through hyperedge HE-7 (induced by source paper "neural graph matching network learning lawler s quadratic assignment problem with extension to hypergraph and multiple graph matching"):
  with members:
    - "exploiting edge features in graph neural networks"
    - "multi graph matching via affinity optimization with graduated consistency regularization"
    - "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"

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)