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: "discriminative active learning"
  Abstract: We propose a new batch mode active learning algorithm designed for neural networks and large query batch sizes. The method, Discriminative Active Learning (DAL), poses active learning as a binary classification task, attempting to choose examples to label in such a way as to make the labeled set and the unlabeled pool indistinguishable. Experimenting on image classification tasks, we empirically show our method to be on par with state of the art methods in medium and large query batch sizes, while being simple to implement and also extend to other domains besides classification tasks. Our experiments also show that none of the state of the art methods of today are clearly better than uncertainty sampling when the batch size is relatively large, negating some of the reported results in the recent literature.

Connected through hyperedge HE-1 (induced by source paper "information condensing active learning"):
  with members:
    - "batchbald efficient and diverse batch acquisition for deep bayesian active learning"
    - "understanding the limitations of variational mutual information estimators"
    - "deep batch active learning by diverse uncertain gradient lower bounds"
    - "adversarial active learning for deep networks a margin based approach"
    - "emnist an extension of mnist to handwritten letters"
    - "deep bayesian active learning with image data"
    - "deep active learning over the long tail"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "fine tuning language models from human preferences"):
  with members:
    - "towards coherent and engaging spoken dialog response generation using automatic conversation evaluators"
    - "finding generalizable evidence by learning to convince q a models"
    - "reward learning for efficient reinforcement learning in extractive document summarisation"
    - "sample efficient text summarization using a single pre trained transformer"
    - "towards coherent and cohesive long form text generation"
    - "deep batch active learning by diverse uncertain gradient lower bounds"
    - "way off policy batch deep reinforcement learning of implicit human preferences in dialog"
    - "learning from dialogue after deployment feed yourself chatbot"

Connected through hyperedge HE-3 (induced by source paper "mining gold samples for conditional gans"):
  with members:
    - "training discriminative models to evaluate generative ones"
    - "variational adversarial active learning"
    - "semantic image synthesis with spatially adaptive normalization"
    - "high fidelity image generation with fewer labels"
    - "cgans with projection discriminator"
    - "the relativistic discriminator a key element missing from standard gan"
    - "spectral normalization for generative adversarial networks"
    - "not all samples are created equal deep learning with importance sampling"

Connected through hyperedge HE-4 (induced by source paper "sampling bias in deep active classification an empirical study"):
  with members:
    - "semantic redundancies in image classification datasets the 10 you don t need"
    - "are all training examples created equal an empirical study"
    - "practical obstacles to deploying active learning"
    - "adversarial active learning for deep networks a margin based approach"
    - "an empirical study of example forgetting during deep neural network learning"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "universal language model fine tuning for text classification"
    - "when will gradient methods converge to max margin classifier under relu models"

Connected through hyperedge HE-5 (induced by source paper "bayesian generative active deep learning"):
  with members:
    - "adversarial active learning for deep networks a margin based approach"
    - "scalable and accurate deep learning for electronic health records"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "generative adversarial active learning"
    - "a survey on deep learning in medical image analysis"
    - "deep bayesian active learning with image data"
    - "a bayesian data augmentation approach for learning deep models"
    - "identity mappings in deep residual networks"

Connected through hyperedge HE-6 (induced by source paper "bias aware heapified policy for active learning"):
  with members:
    - "dataset distillation"
    - "active adversarial domain adaptation"
    - "adversarial active learning for deep networks a margin based approach"
    - "meta learning transferable active learning policies by deep reinforcement learning"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "leveraging motion priors in videos for improving human segmentation"
    - "learning how to active learn a deep reinforcement learning approach"
    - "learning active learning from data"

Connected through hyperedge HE-7 (induced by source paper "towards interpretable and learnable risk analysis for entity resolution"):
  with members:
    - "r humo a risk aware human machine cooperation framework for entity resolution with quality guarantees"
    - "deep anomaly detection with outlier exposure"
    - "on calibration of modern neural networks"
    - "holoclean holistic data repairs with probabilistic inference"
    - "concrete problems in ai safety"
    - "why should i trust you explaining the predictions of any classifier"
    - "a baseline for detecting misclassified and out of distribution examples in neural networks"
    - "tensorflow a system for large scale machine 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)