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: "information theoretic semi supervised metric learning via entropy regularization"
  Abstract: We propose a general information-theoretic approach called Seraph (SEmi-supervised metRic leArning Paradigm with Hyper-sparsity) for metric learning that does not rely upon the manifold assumption. Given the probability parameterized by a Mahalanobis distance, we maximize the entropy of that probability on labeled data and minimize it on unlabeled data following entropy regularization, which allows the supervised and unsupervised parts to be integrated in a natural and meaningful way. Furthermore, Seraph is regularized by encouraging a low-rank projection induced from the metric. The optimization of Seraph is solved efficiently and stably by an EM-like scheme with the analytical E-Step and convex M-Step. Experiments demonstrate that Seraph compares favorably with many well-known global and local metric learning methods.

Connected through hyperedge HE-1 (induced by source paper "affinity guided geometric semi supervised metric learning"):
  with members:
    - "deep metric learning with angular loss"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "tunable efficient unitary neural networks eunn and their application to rnns"
    - "facenet a unified embedding for face recognition and clustering"
    - "particular object retrieval with integral max pooling of cnn activations"
    - "deep metric learning via lifted structured feature embedding"
    - "going deeper with convolutions"
    - "matconvnet convolutional neural networks for matlab"

Connected through hyperedge HE-2 (induced by source paper "information theoretic feature transformation learning for brain interfaces"):
  with members:
    - "adversarial deep learning in eeg biometrics"
    - "hierarchical graphical models for context aware hybrid brain machine interfaces"
    - "multivariate extension of matrix based renyi s alpha order entropy functional"
    - "transfer learning in brain computer interfaces with adversarial variational autoencoders"
    - "estimating mutual information for discrete continuous mixtures"

Connected through hyperedge HE-3 (induced by source paper "adaptive regularization via residual smoothing in deep learning optimization"):
  with members:
    - "combining learning rate decay and weight decay with complexity gradient descent part i"
    - "towards understanding regularization in batch normalization"
    - "understanding batch normalization"
    - "audio visual scene analysis with self supervised multisensory features"
    - "regularizing deep neural networks by noise its interpretation and optimization"
    - "unsupervised learning of depth and ego motion from video"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "deep convolutional neural network for inverse problems in imaging"

Connected through hyperedge HE-4 (induced by source paper "deep metric learning with density adaptivity"):
  with members:
    - "deep semantic hashing with generative adversarial networks"
    - "learning spread out local feature descriptors"
    - "understanding deep learning requires rethinking generalization"
    - "deep metric learning via facility location"
    - "local similarity aware deep feature embedding"
    - "learning deep embeddings with histogram loss"
    - "hard aware deeply cascaded embedding"
    - "fine grained categorization and dataset bootstrapping using deep metric learning with humans in the loop"

Connected through hyperedge HE-5 (induced by source paper "classification from pairwise similarities dissimilarities and unlabeled data via empirical risk minimization"):
  with members:
    - "classification from pairwise similarity and unlabeled data"
    - "semi supervised classification based on classification from positive and unlabeled data"

Connected through hyperedge HE-6 (induced by source paper "classification from triplet comparison data"):
  with members:
    - "classification from pairwise similarities dissimilarities and unlabeled data via empirical risk minimization"
    - "classification from pairwise similarity and unlabeled data"

Connected through hyperedge HE-7 (induced by source paper "classification from pairwise similarity and unlabeled data"):
  with members:
    - "positive unlabeled learning with non negative risk estimator"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "convex formulation of multiple instance learning from positive and unlabeled bags"
    - "learning from video and text via large scale discriminative clustering"
    - "theoretical comparisons of positive unlabeled learning against positive negative learning"
    - "mixture proportion estimation via kernel embedding of distributions"
    - "semi supervised information maximization clustering"
    - "clustering partially observed graphs via convex optimization"

Connected through hyperedge HE-8 (induced by source paper "lifelong metric learning"):
  with members:
    - "sparse compositional metric learning"
    - "a survey on metric learning for feature vectors and structured data"
    - "learning task grouping and overlap in multi task learning"

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)