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 co training"
  Abstract: This paper introduces an information theoretic co-training objective for unsupervised learning. We consider the problem of predicting the future. Rather than predict future sensations (image pixels or sound waves) we predict ``hypotheses'' to be confirmed by future sensations. More formally, we assume a population distribution on pairs $(x,y)$ where we can think of $x$ as a past sensation and $y$ as a future sensation. We train both a predictor model $P_\Phi(z|x)$ and a confirmation model $P_\Psi(z|y)$ where we view $z$ as hypotheses (when predicted) or facts (when confirmed). For a population distribution on pairs $(x,y)$ we focus on the problem of measuring the mutual information between $x$ and $y$. By the data processing inequality this mutual information is at least as large as the mutual information between $x$ and $z$ under the distribution on triples $(x,z,y)$ defined by the confirmation model $P_\Psi(z|y)$. The information theoretic training objective for $P_\Phi(z|x)$ and $P_\Psi(z|y)$ can be viewed as a form of co-training where we want the prediction from $x$ to match the confirmation from $y$. We give experiments on applications to learning phonetics on the TIMIT dataset.

Connected through hyperedge HE-1 (induced by source paper "putting an end to end to end gradient isolated learning of representations"):
  with members:
    - "data efficient image recognition with contrastive predictive coding"
    - "greedy layerwise learning can scale to imagenet"
    - "error forward propagation reusing feedforward connections to propagate errors in deep learning"
    - "biologically plausible learning algorithms can scale to large datasets"
    - "auto encoding total correlation explanation"
    - "i revnet deep invertible networks"
    - "conducting credit assignment by aligning local representations"
    - "representation learning with contrastive predictive coding"

Connected through hyperedge HE-2 (induced by source paper "calibration entropy rates and memory in language models"):
  with members:
    - "direct output connection for a high rank language model"
    - "frage frequency agnostic word representation"
    - "an analysis of neural language modeling at multiple scales"
    - "learning longer term dependencies in rnns with auxiliary losses"
    - "sharp nearby fuzzy far away how neural language models use context"
    - "on calibration of modern neural networks"
    - "on the state of the art of evaluation in neural language models"
    - "topic compositional neural language model"

Connected through hyperedge HE-3 (induced by source paper "unsupervised discovery of temporal structure in noisy data with dynamical components analysis"):
  with members:
    - "formal limitations on the measurement of mutual information"
    - "protecting sensory data against sensitive inferences"
    - "representation learning with contrastive predictive coding"
    - "graph based predictable feature analysis"
    - "estimating mixture entropy with pairwise distances"
    - "unsupervised visual representation learning by context prediction"
    - "character aware neural language models"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-4 (induced by source paper "formal limitations on the measurement of mutual information"):
  with members:
    - "mine mutual information neural estimation"
    - "representation learning with contrastive predictive coding"
    - "computing nonvacuous generalization bounds for deep stochastic neural networks with many more parameters than training data"
    - "efficient estimation of mutual information for strongly dependent variables"
    - "a pac bayesian tutorial with a dropout bound"

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)