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: "metric and kernel learning using a linear transformation"
  Abstract: Metric and kernel learning are important in several machine learning applications. However, most existing metric learning algorithms are limited to learning metrics over low-dimensional data, while existing kernel learning algorithms are often limited to the transductive setting and do not generalize to new data points. In this paper, we study metric learning as a problem of learning a linear transformation of the input data. We show that for high-dimensional data, a particular framework for learning a linear transformation of the data based on the LogDet divergence can be efficiently kernelized to learn a metric (or equivalently, a kernel function) over an arbitrarily high dimensional space. We further demonstrate that a wide class of convex loss functions for learning linear transformations can similarly be kernelized, thereby considerably expanding the potential applications of metric learning. We demonstrate our learning approach by applying it to large-scale real world problems in computer vision and text mining.

Connected through hyperedge HE-1 (induced by source paper "multi modal music information retrieval augmenting audio analysis with visual computing for improved music video analysis"):
  with members:
    - "predicting time to failure of plasma etching equipment using machine learning"
    - "cross modal music retrieval and applications an overview of key methodologies"
    - "multi task music representation learning from multi label embeddings"
    - "fashion and apparel classification using convolutional neural networks"
    - "a multi modal deep neural network approach to bird song identification"
    - "large scale audio visual video analytics platform for forensic investigations of terroristic attacks"
    - "multi temporal resolution convolutional neural networks for acoustic scene classification"
    - "content based video music retrieval using soft intra modal structure constraint"

Connected through hyperedge HE-2 (induced by source paper "a deep convolutional network for seismic shot gather image quality classification"):
  with members:
    - "cbam convolutional block attention module"
    - "squeeze and excitation networks"
    - "understanding neural networks through deep visualization"
    - "deep residual learning for image recognition"
    - "rethinking the inception architecture for computer vision"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "deep metric learning using triplet network"

Connected through hyperedge HE-3 (induced by source paper "robust rgb d face recognition using attribute aware loss"):
  with members:
    - "arcface additive angular margin loss for deep face recognition"
    - "additive margin softmax for face verification"
    - "normface l 2 hypersphere embedding for face verification"
    - "learning from millions of 3d scans for large scale 3d face recognition"
    - "sphereface deep hypersphere embedding for face recognition"
    - "cnn based real time dense face reconstruction with inverse rendered photo realistic face images"
    - "l2 constrained softmax loss for discriminative face verification"
    - "deep 3d face identification"

Connected through hyperedge HE-4 (induced by source paper "asymmetric kernel in gaussian processes for learning target variance"):
  with members:
    - "kernel regression with sparse metric learning"
    - "counting everyday objects in everyday scenes"
    - "deep residual learning for image recognition"
    - "imagenet large scale visual recognition challenge"
    - "gaussian processes for big data"
    - "greedy block coordinate descent for large scale gaussian process regression"

Connected through hyperedge HE-5 (induced by source paper "ts net combining modality specific and common features for multimodal patch matching"):
  with members:
    - "pn net conjoined triple deep network for learning local image descriptors"
    - "stereo matching by training a convolutional neural network to compare image patches"
    - "learning local image descriptors with deep siamese and triplet convolutional networks by minimising global loss functions"
    - "learning to compare image patches via convolutional neural networks"

Connected through hyperedge HE-6 (induced by source paper "fashion and apparel classification using convolutional neural networks"):
  with members:
    - "deep residual learning for image recognition"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "return of the devil in the details delving deep into convolutional nets"

Connected through hyperedge HE-7 (induced by source paper "revealing the basis ordinal embedding through geometry"):
  with members:
    - "human powered sorts and joins"
    - "noisy sorting without resampling"

Connected through hyperedge HE-8 (induced by source paper "a fast and easy regression technique for k nn classification without using negative pairs"):
  with members:
    - "ridge regression hubness and zero shot learning"
    - "a survey on metric learning for feature vectors and structured data"

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.LG(Machine Learning)