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: "multivariate information bottleneck"
  Abstract: The Information bottleneck method is an unsupervised non-parametric data organization technique. Given a joint distribution P(A,B), this method constructs a new variable T that extracts partitions, or clusters, over the values of A that are informative about B. The information bottleneck has already been applied to document classification, gene expression, neural code, and spectral analysis. In this paper, we introduce a general principled framework for multivariate extensions of the information bottleneck method. This allows us to consider multiple systems of data partitions that are inter-related. Our approach utilizes Bayesian networks for specifying the systems of clusters and what information each captures. We show that this construction provides insight about bottleneck variations and enables us to characterize solutions of these variations. We also present a general framework for iterative algorithms for constructing solutions, and apply it to several examples.

Connected through hyperedge HE-1 (induced by source paper "dynamics aware unsupervised discovery of skills"):
  with members:
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "therml the thermodynamics of machine learning"
    - "model ensemble trust region policy optimization"
    - "soft actor critic algorithms and applications"
    - "diversity is all you need learning skills without a reward function"
    - "unsupervised control through non parametric discriminative rewards"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"

Connected through hyperedge HE-2 (induced by source paper "information bottleneck and its applications in deep learning"):
  with members:
    - "a direct sum result for the information complexity of learning"
    - "chaining mutual information and tightening generalization bounds"
    - "mathematics of deep learning"
    - "infovae information maximizing variational autoencoders"
    - "emergence of invariance and disentanglement in deep representations"
    - "stochastic gradient descent performs variational inference converges to limit cycles for deep networks"
    - "fixing a broken elbo"
    - "sharp minima can generalize for deep nets"

Connected through hyperedge HE-3 (induced by source paper "lautum regularization for semi supervised transfer learning"):
  with members:
    - "entropy and mutual information in models of deep neural networks"
    - "semi supervised deep kernel learning regression with unlabeled data by minimizing predictive variance"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "specializing word embeddings for parsing by information bottleneck"):
  with members:
    - "assessing bert s syntactic abilities"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "on variational bounds of mutual information"
    - "towards better ud parsing deep contextualized word embeddings ensemble and treebank concatenation"
    - "allennlp a deep semantic natural language processing platform"
    - "deep contextualized word representations"
    - "efficient human like semantic representations via the information bottleneck principle"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-5 (induced by source paper "factorized mutual information maximization"):
  with members:
    - "large scale study of curiosity driven learning"
    - "deep variational information bottleneck"
    - "information theoretically aided reinforcement learning for embodied agents"
    - "a theory of cheap control in embodied systems"
    - "efficient estimation of mutual information for strongly dependent variables"
    - "finding the maximizers of the information divergence from an exponential family"
    - "higher coordination with less control a result of information maximization in the sensorimotor loop"

Connected through hyperedge HE-6 (induced by source paper "invariant information clustering for unsupervised image classification and segmentation"):
  with members:
    - "deep clustering for unsupervised learning of visual features"
    - "mine mutual information neural estimation"
    - "deeppermnet visual permutation learning"
    - "scaling the scattering transform deep hybrid networks"
    - "learning to segment every thing"
    - "unsupervised representation learning by sorting sequences"
    - "improved regularization of convolutional neural networks with cutout"
    - "deep unsupervised similarity learning using partially ordered sets"

Connected through hyperedge HE-7 (induced by source paper "bregman divergence bounds and universality properties of the logarithmic loss"):
  with members:
    - "on f divergences integral representations local behavior and inequalities"
    - "on the universality of the logistic loss function"
    - "universality of logarithmic loss in lossy compression"
    - "opening the black box of deep neural networks via information"
    - "f divergence inequalities"
    - "generalized independent component analysis over finite alphabets"
    - "justification of logarithmic loss via the benefit of side information"
    - "deep learning and the information bottleneck principle"

Connected through hyperedge HE-8 (induced by source paper "the color of smiling computational synaesthesia of facial expressions"):
  with members:
    - "affective facial expression processing via simulation a probabilistic model"

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)