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: "nearest neighbour radial basis function solvers for deep neural networks"
  Abstract: We present a radial basis function solver for convolutional neural networks that can be directly applied to both distance metric learning and classification problems. Our method treats all training features from a deep neural network as radial basis function centres and computes loss by summing the influence of a feature's nearby centres in the embedding space. Having a radial basis function centred on each training feature is made scalable by treating it as an approximate nearest neighbour search problem. End-to-end learning of the network and solver is carried out, mapping high dimensional features into clusters of the same class. This results in a well formed embedding space, where semantically related instances are likely to be located near one another, regardless of whether or not the network was trained on those classes. The same loss function is used for both the metric learning and classification problems. We show that our radial basis function solver outperforms state-of-the-art embedding approaches on the Stanford Cars196 and CUB-200-2011 datasets. Additionally, we show that when used as a classifier, our method outperforms a conventional softmax classifier on the CUB-200-2011, Stanford Cars196, Oxford 102 Flowers and Leafsnap fine-grained classification datasets.

Connected through hyperedge HE-1 (induced by source paper "fast fine grained image classification via weakly supervised discriminative localization"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "fine grained discriminative localization via saliency guided faster r cnn"
    - "diversified visual attention networks for fine grained object classification"
    - "paying more attention to attention improving the performance of convolutional neural networks via attention transfer"
    - "localizing by describing attribute guided attention localization for fine grained recognition"
    - "weakly supervised fine grained categorization with part based image representation"
    - "learning a discriminative filter bank within a cnn for fine grained recognition"
    - "interactive inter layer activeness propagation"

Connected through hyperedge HE-2 (induced by source paper "mining on manifolds metric learning without labels"):
  with members:
    - "polysemous codes"
    - "parametric manifold learning via sparse multidimensional scaling"
    - "sampling matters in deep embedding learning"
    - "transitive invariance for self supervised visual representation learning"
    - "self supervised learning of visual features through embedding images into text topic spaces"
    - "iterative manifold embedding layer learned by incomplete data for large scale image retrieval"
    - "multi task self supervised visual learning"
    - "no fuss distance metric learning using proxies"

Connected through hyperedge HE-3 (induced by source paper "impostor networks for fast fine grained recognition"):
  with members:
    - "generalized orderless pooling performs implicit salient matching"
    - "billion scale similarity search with gpus"
    - "no fuss distance metric learning using proxies"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "the inaturalist species classification and detection dataset"
    - "lcnn lookup based convolutional neural network"
    - "learning structured sparsity in deep neural networks"
    - "deep image retrieval learning global representations for image search"

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)