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: "semi supervised kernel metric learning using relative comparisons"
  Abstract: We consider the problem of metric learning subject to a set of constraints on relative-distance comparisons between the data items. Such constraints are meant to reflect side-information that is not expressed directly in the feature vectors of the data items. The relative-distance constraints used in this work are particularly effective in expressing structures at finer level of detail than must-link (ML) and cannot-link (CL) constraints, which are most commonly used for semi-supervised clustering. Relative-distance constraints are thus useful in settings where providing an ML or a CL constraint is difficult because the granularity of the true clustering is unknown. #R##N#Our main contribution is an efficient algorithm for learning a kernel matrix using the log determinant divergence --- a variant of the Bregman divergence --- subject to a set of relative-distance constraints. The learned kernel matrix can then be employed by many different kernel methods in a wide range of applications. In our experimental evaluations, we consider a semi-supervised clustering setting and show empirically that kernels found by our algorithm yield clusterings of higher quality than existing approaches that either use ML/CL constraints or a different means to implement the supervision using relative comparisons.

Connected through hyperedge HE-1 (induced by source paper "multi class classification without multi class labels"):
  with members:
    - "classification from pairwise similarity and unlabeled data"
    - "realistic evaluation of deep semi supervised learning algorithms"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "visualizing the loss landscape of neural nets"
    - "learning to cluster in order to transfer across domains and tasks"
    - "identity mappings in deep residual networks"
    - "matching networks for one shot learning"
    - "an empirical analysis of the optimization of deep network loss surfaces"

Connected through hyperedge HE-2 (induced by source paper "a probabilistic constrained clustering for transfer learning and image category discovery"):
  with members:
    - "learning to cluster in order to transfer across domains and tasks"
    - "large scale object discovery and detector adaptation from unlabeled video"
    - "identity mappings in deep residual networks"
    - "deep image category discovery using a transferred similarity function"
    - "neural network based clustering using pairwise constraints"
    - "very deep convolutional networks for large scale image recognition"
    - "on constrained spectral clustering and its applications"

Connected through hyperedge HE-3 (induced by source paper "adaptive image stream classification via convolutional neural network with intrinsic similarity metrics"):
  with members:
    - "doc deep open classification of text documents"
    - "emnist an extension of mnist to handwritten letters"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "facenet a unified embedding for face recognition and clustering"
    - "frequent directions simple and deterministic matrix sketching"
    - "a survey on metric learning for feature vectors and structured data"

Connected through hyperedge HE-4 (induced by source paper "learning to cluster in order to transfer across domains and tasks"):
  with members:
    - "adversarial discriminative domain adaptation"
    - "asymmetric tri training for unsupervised domain adaptation"
    - "deep transfer learning with joint adaptation networks"
    - "unsupervised domain adaptation with residual transfer networks"
    - "learning transferable features with deep adaptation networks"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "learning to compare image patches via convolutional 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)