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: "socratic learning augmenting generative models to incorporate latent subsets in training data"
  Abstract: A challenge in training discriminative models like neural networks is obtaining enough labeled training data. Recent approaches use generative models to combine weak supervision sources, like user-defined heuristics or knowledge bases, to label training data. Prior work has explored learning accuracies for these sources even without ground truth labels, but they assume that a single accuracy parameter is sufficient to model the behavior of these sources over the entire training set. In particular, they fail to model latent subsets in the training data in which the supervision sources perform differently than on average. We present Socratic learning, a paradigm that uses feedback from a corresponding discriminative model to automatically identify these subsets and augments the structure of the generative model accordingly. Experimentally, we show that without any ground truth labels, the augmented generative model reduces error by up to 56.06% for a relation extraction task compared to a state-of-the-art weak supervision technique that utilizes generative models.

Connected through hyperedge HE-1 (induced by source paper "zero shot task transfer"):
  with members:
    - "layoutnet reconstructing the 3d room layout from a single rgb image"
    - "latent ransac"
    - "bayesian model agnostic meta learning"
    - "taskonomy disentangling task transfer learning"
    - "adversarial data programming using gans to relax the bottleneck of curated labeled data"
    - "domain adaptation for visual applications a comprehensive survey"
    - "maximum classifier discrepancy for unsupervised domain adaptation"
    - "zero shot task generalization with multi task deep reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "pairwise feedback for data programming"):
  with members:
    - "learning the structure of generative models without labeled data"

Connected through hyperedge HE-3 (induced by source paper "diag nre a neural pattern diagnosis framework for distantly supervised neural relation extraction"):
  with members:
    - "robust distant supervision relation extraction via deep reinforcement learning"
    - "reinforcement learning for relation classification from noisy data"
    - "neural open information extraction"
    - "metapad meta pattern discovery from massive text corpora"
    - "learning with noise enhance distantly supervised relation extraction with dynamic transition matrix"
    - "snorkel rapid training data creation with weak supervision"
    - "heterogeneous supervision for relation extraction a representation learning approach"
    - "learning the structure of generative models without labeled data"

Connected through hyperedge HE-4 (induced by source paper "training classifiers with natural language explanations"):
  with members:
    - "naturalizing a programming language via interactive learning"
    - "snorkel rapid training data creation with weak supervision"
    - "teaching machines to describe images via natural language feedback"
    - "learning executable semantic parsers for natural language understanding"
    - "learning through dialogue interactions by asking questions"
    - "dialog based language learning"
    - "rationalizing neural predictions"
    - "learning to map sentences to logical form structured classification with probabilistic categorial grammars"

Connected through hyperedge HE-5 (induced by source paper "simple large scale relation extraction from unstructured text"):
  with members:
    - "bag of tricks for efficient text classification"
    - "improving hypernymy detection with an integrated path based and distributional method"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-6 (induced by source paper "fidelity weighted learning"):
  with members:
    - "neural ranking models with weak supervision"
    - "holoclean holistic data repairs with probabilistic inference"
    - "decoupling when to update from how to update"
    - "learning from noisy large scale datasets with minimal supervision"
    - "learning to attend copy and generate for session based query suggestion"
    - "avoiding your teacher s mistakes training neural networks with controlled weak supervision"
    - "loss factorization weakly supervised learning and label noise robustness"
    - "adversarial feature learning"

Connected through hyperedge HE-7 (induced by source paper "avoiding your teacher s mistakes training neural networks with controlled weak supervision"):
  with members:
    - "progressive neural networks"
    - "leveraging large amounts of weakly supervised data for multi language sentiment classification"
    - "neural ranking models with weak supervision"
    - "holoclean holistic data repairs with probabilistic inference"
    - "learning from noisy large scale datasets with minimal supervision"
    - "learning to attend copy and generate for session based query suggestion"
    - "tf learn tensorflow s high level module for distributed machine learning"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-8 (induced by source paper "infrastructure for usable machine learning the stanford dawn project"):
  with members:
    - "asap prioritizing attention via time series smoothing"
    - "learning the structure of generative models without labeled data"
    - "noscope optimizing neural network queries over video at scale"
    - "macrobase prioritizing attention in fast data"
    - "omnivore an optimizer for multi device deep learning on cpus and gpus"
    - "a two pronged progress in structured dense matrix multiplication"
    - "slimfast guaranteed results for data fusion and source reliability"
    - "worst case optimal join algorithms"

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)