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: "a survey on metric learning for feature vectors and structured data"
  Abstract: The need for appropriate ways to measure the distance or similarity between data is ubiquitous in machine learning, pattern recognition and data mining, but handcrafting such good metrics for specific problems is generally difficult. This has led to the emergence of metric learning, which aims at automatically learning a metric from data and has attracted a lot of interest in machine learning and related fields for the past ten years. This survey paper proposes a systematic review of the metric learning literature, highlighting the pros and cons of each approach. We pay particular attention to Mahalanobis distance metric learning, a well-studied and successful framework, but additionally present a wide range of methods that have recently emerged as powerful alternatives, including nonlinear metric learning, similarity learning and local metric learning. Recent trends and extensions, such as semi-supervised metric learning, metric learning for histogram data and the derivation of generalization guarantees, are also covered. Finally, this survey addresses metric learning for structured data, in particular edit distance learning, and attempts to give an overview of the remaining challenges in metric learning for the years to come.

Connected through hyperedge HE-1 (induced by source paper "an overview of distance and similarity functions for structured data"):
  with members:
    - "graph matching networks for learning the similarity of graph structured objects"
    - "relational inductive biases deep learning and graph networks"
    - "how powerful are graph neural networks"
    - "an empirical evaluation of similarity measures for time series classification"
    - "time warp edit distance with stiffness adjustment for time series matching"
    - "support vector machine classification with indefinite kernels"
    - "semantic similarity in a taxonomy an information based measure and its application to problems of ambiguity in natural language"

Connected through hyperedge HE-2 (induced by source paper "metric learning for ordered labeled trees with pq grams"):
  with members:
    - "tree edit distance learning via adaptive symbol embeddings"
    - "revisiting the tree edit distance and its backtracing a tutorial"
    - "adam a method for stochastic optimization"
    - "a conditional random field for discriminatively trained finite state string edit distance"
    - "an optimal decomposition algorithm for tree edit distance"

Connected through hyperedge HE-3 (induced by source paper "multiple metric learning for structured data"):
  with members:
    - "a projection method for metric constrained optimization"
    - "semi supervised classification with graph convolutional networks"
    - "sparse compositional metric learning"

Connected through hyperedge HE-4 (induced by source paper "learning cost functions for optimal transport"):
  with members:
    - "learning transport cost from subset correspondence"
    - "learning with batch wise optimal transport loss for 3d shape recognition"
    - "parameter estimation in finite mixture models by regularized optimal transport a unified framework for hard and soft clustering"

Connected through hyperedge HE-5 (induced by source paper "optimizing rank based metrics with blackbox differentiation"):
  with members:
    - "learning with average precision training image retrieval with a listwise loss"
    - "generalized intersection over union a metric and a loss for bounding box regression"
    - "towards accurate one stage object detection with ap loss"
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "sodeep a sorting deep net to learn ranking loss surrogates"
    - "mediaeval 2018 predicting media memorability task"
    - "deep metric learning with bier boosting independent embeddings robustly"
    - "yolov3 an incremental improvement"

Connected through hyperedge HE-6 (induced by source paper "revisiting metric learning for few shot image classification"):
  with members:
    - "dense classification and implanting for few shot learning"
    - "multi level semantic feature augmentation for one shot learning"
    - "meta learning with differentiable convex optimization"
    - "lgm net learning to generate matching networks for few shot learning"
    - "few shot learning with localization in realistic settings"
    - "revisiting local descriptor based image to class measure for few shot learning"
    - "edge labeling graph neural network for few shot learning"
    - "finding task relevant features for few shot learning by category traversal"

Connected through hyperedge HE-7 (induced by source paper "locality aware appearance metric for multi target multi camera tracking"):
  with members:
    - "multiple hypothesis tracking algorithm for multi target multi camera tracking with disjoint views"
    - "cityflow a city scale benchmark for multi target multi camera vehicle tracking and re identification"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "features for multi target multi camera tracking and re identification"
    - "improving online multiple object tracking with deep metric learning"
    - "multiple target tracking by learning feature representation and distance metric jointly"
    - "in defense of the triplet loss for person re identification"
    - "random erasing data augmentation"

Connected through hyperedge HE-8 (induced by source paper "context aware visual compatibility prediction"):
  with members:
    - "graph convolutional neural networks for web scale recommender systems"
    - "learning type aware embeddings for fashion compatibility"
    - "relational inductive biases deep learning and graph networks"
    - "recommending outfits from personal closet"
    - "outfit generation and style extraction via bidirectional lstm and autoencoder"
    - "prototypical networks for few shot learning"
    - "creating capsule wardrobes from fashion images"
    - "geometric matrix completion with recurrent multi graph neural networks"

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)