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: "sparse compositional metric learning"
  Abstract: We propose a new approach for metric learning by framing it as learning a sparse combination of locally discriminative metrics that are inexpensive to generate from the training data. This flexible framework allows us to naturally derive formulations for global, multi-task and local metric learning. The resulting algorithms have several advantages over existing methods in the literature: a much smaller number of parameters to be estimated and a principled way to generalize learned metrics to new testing data points. To analyze the approach theoretically, we derive a generalization bound that justifies the sparse combination. Empirically, we evaluate our algorithms on several datasets against state-of-the-art metric learning methods. The results are consistent with our theoretical findings and demonstrate the superiority of our approach in terms of classification performance and scalability.

Connected through hyperedge HE-1 (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"
    - "a survey on metric learning for feature vectors and structured data"

Connected through hyperedge HE-2 (induced by source paper "a probabilistic approach for learning embeddings without supervision"):
  with members:
    - "crdoco pixel level domain transfer with cross domain consistency"
    - "hardness aware deep metric learning"
    - "few shot learning with localization in realistic settings"
    - "unsupervised embedding learning via invariant and spreading instance feature"
    - "generalized zero and few shot learning via aligned variational autoencoders"
    - "deep clustering for unsupervised learning of visual features"
    - "adversarial metric learning"
    - "mining on manifolds metric learning without labels"

Connected through hyperedge HE-3 (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-4 (induced by source paper "adaptive nearest neighbor a general framework for distance metric learning"):
  with members:
    - "learning local metrics and influential regions for classification"
    - "generalized cross entropy loss for training deep neural networks with noisy labels"
    - "fine grained categorization and dataset bootstrapping using deep metric learning with humans in the loop"
    - "facenet a unified embedding for face recognition and clustering"
    - "deep metric learning via lifted structured feature embedding"

Connected through hyperedge HE-5 (induced by source paper "robust metric learning based on the rescaled hinge loss"):
  with members:
    - "embracing error to enable rapid crowdsourcing"

Connected through hyperedge HE-6 (induced by source paper "learning local metrics and influential regions for classification"):
  with members:
    - "sample complexity of learning mahalanobis distance metrics"
    - "generalization bounds for metric and similarity learning"

Connected through hyperedge HE-7 (induced by source paper "lifelong metric learning"):
  with members:
    - "a survey on metric learning for feature vectors and structured data"
    - "information theoretic semi supervised metric learning via entropy regularization"
    - "learning task grouping and overlap in multi task learning"

Connected through hyperedge HE-8 (induced by source paper "constraint selection in metric learning"):
  with members:
    - "deep metric learning via lifted structured feature embedding"
    - "parametric local metric learning for nearest neighbor classification"
    - "similarity based learning via data driven embeddings"
    - "positive semidefinite metric learning using boosting like algorithms"
    - "metric and kernel learning using a linear transformation"

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)