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 deep kernel learning regression with unlabeled data by minimizing predictive variance"
  Abstract: Large amounts of labeled data are typically required to train deep learning models. For many real-world problems, however, acquiring additional data can be expensive or even impossible. We present semi-supervised deep kernel learning (SSDKL), a semi-supervised regression model based on minimizing predictive variance in the posterior regularization framework. SSDKL combines the hierarchical representation learning of neural networks with the probabilistic modeling capabilities of Gaussian processes. By leveraging unlabeled data, we show improvements on a diverse set of real-world regression tasks over supervised deep kernel learning and semi-supervised methods such as VAT and mean teacher adapted for regression.

Connected through hyperedge HE-1 (induced by source paper "post estimation smoothing a simple baseline for learning with side information"):
  with members:
    - "predicting 3d human dynamics from video"
    - "are we really making much progress a worrying analysis of recent neural recommendation approaches"
    - "simple random search provides a competitive approach to reinforcement learning"
    - "learning 3d human dynamics from video"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "end to end recovery of human shape and pose"
    - "learning 3d human pose from structure and motion"
    - "xgboost a scalable tree boosting system"

Connected through hyperedge HE-2 (induced by source paper "a simple yet effective baseline for robust deep learning with noisy labels"):
  with members:
    - "dimensionality driven learning with noisy labels"
    - "co teaching robust training of deep neural networks with extremely noisy labels"
    - "autoaugment learning augmentation policies from data"
    - "learning to reweight examples for robust deep learning"
    - "generalized cross entropy loss for training deep neural networks with noisy labels"
    - "using trusted data to train deep networks on labels corrupted by severe noise"
    - "cleannet transfer learning for scalable image classifier training with label noise"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"

Connected through hyperedge HE-3 (induced by source paper "learning to impute a general framework for semi supervised learning"):
  with members:
    - "meta learning with implicit gradients"
    - "unsupervised data augmentation for consistency training"
    - "learning to learn via self critique"
    - "mixmatch a holistic approach to semi supervised learning"
    - "learning to self train for semi supervised few shot classification"
    - "self tuning networks bilevel optimization of hyperparameters using structured best response functions"
    - "meta learning for semi supervised few shot classification"
    - "unsupervised learning via meta learning"

Connected through hyperedge HE-4 (induced by source paper "lautum regularization for semi supervised transfer learning"):
  with members:
    - "entropy and mutual information in models of deep neural networks"
    - "taskonomy disentangling task transfer learning"
    - "a survey on deep transfer learning"
    - "semi supervised learning with declaratively specified entropy constraints"
    - "generalization error in deep learning"
    - "explicit inductive bias for transfer learning with convolutional networks"
    - "when semi supervised learning meets transfer learning training strategies models and datasets"
    - "realistic evaluation of deep semi supervised learning algorithms"

Connected through hyperedge HE-5 (induced by source paper "deep probabilistic kernels for sample efficient learning"):
  with members:
    - "sampled softmax with random fourier features"
    - "a closer look at few shot classification"
    - "infrastructure quality assessment in africa using satellite imagery and deep learning"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "prototypical networks for few shot learning"
    - "learning to compare relation network for few shot learning"
    - "deep bayesian active learning with image data"
    - "model agnostic meta learning for fast adaptation of deep networks"

Connected through hyperedge HE-6 (induced by source paper "reliable estimation of individual treatment effect with causal information bottleneck"):
  with members:
    - "adversarial balancing based representation learning for causal effect inference with observational data"
    - "improving generalization and stability of generative adversarial networks"
    - "uncertainty in the variational information bottleneck"
    - "cycada cycle consistent adversarial domain adaptation"
    - "bayesian inference of individualized treatment effects using multi task gaussian processes"
    - "adversarial discriminative domain adaptation"
    - "deep variational information bottleneck"
    - "adam a method for stochastic optimization"

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)