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: "similarity learning for provably accurate sparse linear classification"
  Abstract: In recent years, the crucial importance of metrics in machine learning algorithms has led to an increasing interest for optimizing distance and similarity functions. Most of the state of the art focus on learning Mahalanobis distances (requiring to fulfill a constraint of positive semi-definiteness) for use in a local k-NN algorithm. However, no theoretical link is established between the learned metrics and their performance in classification. In this paper, we make use of the formal framework of good similarities introduced by Balcan et al. to design an algorithm for learning a non PSD linear similarity optimized in a nonlinear feature space, which is then used to build a global linear classifier. We show that our approach has uniform stability and derive a generalization bound on the classification error. Experiments performed on various datasets confirm the effectiveness of our approach compared to state-of-the-art methods and provide evidence that (i) it is fast, (ii) robust to overfitting and (iii) produces very sparse classifiers.

Connected through hyperedge HE-1 (induced by source paper "contrastive multiview coding"):
  with members:
    - "learning robust and multilingual speech representations"
    - "self supervised learning of video induced visual invariances"
    - "end to end learning of visual representations from uncurated instructional videos"
    - "momentum contrast for unsupervised visual representation learning"
    - "large scale adversarial representation learning"
    - "randaugment practical data augmentation with no separate search"
    - "a survey of multi view representation learning"
    - "data efficient image recognition with contrastive predictive coding"

Connected through hyperedge HE-2 (induced by source paper "fast low rank metric learning for large scale and high dimensional data"):
  with members:
    - "parts4feature learning 3d global features from generally semantic parts in multiple views"
    - "3dviewgraph learning global features for 3d shapes from a graph of unordered views with attention"
    - "l2g auto encoder understanding point clouds by local to global reconstruction with hierarchical self attention"
    - "multi angle point cloud vae unsupervised feature learning for 3d point clouds from multiple angles by joint self reconstruction and half to half prediction"
    - "y 2seq2seq cross modal representation learning for 3d shape and text by joint reconstruction and prediction of view and word sequences"
    - "view inter prediction gan unsupervised representation learning for 3d shapes by learning global shape memories to support local view predictions"
    - "online learning with an unknown fairness metric"
    - "point2sequence learning the shape representation of 3d point clouds with an attention based sequence to sequence network"

Connected through hyperedge HE-3 (induced by source paper "a theoretical analysis of contrastive unsupervised representation learning"):
  with members:
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "unsupervised learning of sentence embeddings using compositional n gram features"
    - "an efficient framework for learning sentence representations"
    - "provable benefits of representation learning"
    - "prototypical networks for few shot learning"
    - "a non generative framework and convex relaxations for unsupervised learning"
    - "a vector contraction inequality for rademacher complexities"

Connected through hyperedge HE-4 (induced by source paper "mirbot a collaborative object recognition system for smartphones using convolutional neural networks"):
  with members:
    - "scnn an accelerator for compressed sparse convolutional neural networks"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "xception deep learning with depthwise separable convolutions"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "mxnet a flexible and efficient machine learning library for heterogeneous distributed systems"
    - "rethinking the inception architecture for computer vision"
    - "how transferable are features in deep neural networks"

Connected through hyperedge HE-5 (induced by source paper "binary classification in unstructured space with hypergraph case based reasoning"):
  with members:
    - "a unified approach to interpreting model predictions"
    - "classification with boosting of extreme learning machine over arbitrarily partitioned data"
    - "why should i trust you explaining the predictions of any classifier"
    - "deep learning in neural networks"
    - "a survey on metric learning for feature vectors and structured data"
    - "improved use of continuous attributes in c4 5"

Connected through hyperedge HE-6 (induced by source paper "robustness and generalization for metric learning"):
  with members:
    - "sparse compositional metric learning"
    - "a survey on metric learning for feature vectors and structured data"
    - "guaranteed classification via regularized similarity learning"
    - "generalization bounds for metric and similarity learning"
    - "similarity based learning via data driven embeddings"
    - "robustness and generalization"

Connected through hyperedge HE-7 (induced by source paper "sample complexity of learning mahalanobis distance metrics"):
  with members:
    - "robustness and generalization for metric learning"
    - "a survey on metric learning for feature vectors and structured data"
    - "guaranteed classification via regularized similarity learning"
    - "generalization bounds for metric and similarity learning"

Connected through hyperedge HE-8 (induced by source paper "a survey on metric learning for feature vectors and structured data"):
  with members:
    - "robustness and generalization for metric learning"
    - "efficient distance metric learning by adaptive sampling and mini batch stochastic gradient descent sgd"
    - "large margin metric learning for partitioning problems"
    - "on the generalization ability of online learning algorithms for pairwise loss functions"
    - "guaranteed classification via regularized similarity learning"
    - "parametric local metric learning for nearest neighbor classification"
    - "a conditional random field for discriminatively trained finite state string edit distance"
    - "supervised metric learning with generalization guarantees"

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)