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: "deep batch active learning by diverse uncertain gradient lower bounds"
  Abstract: We design a new algorithm for batch active learning with deep neural network models. Our algorithm, Batch Active learning by Diverse Gradient Embeddings (BADGE), samples groups of points that are disparate and high-magnitude when represented in a hallucinated gradient space, a strategy designed to incorporate both predictive uncertainty and sample diversity into every selected batch. Crucially, BADGE trades off between diversity and uncertainty without requiring any hand-tuned hyperparameters. We show that while other approaches sometimes succeed for particular batch sizes or architectures, BADGE consistently performs as well or better, making it a versatile option for practical active learning problems.

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"
    - "adversarial active learning for deep networks a margin based approach"
    - "discriminative active learning"
    - "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"
    - "way off policy batch deep reinforcement learning of implicit human preferences in dialog"
    - "learning from dialogue after deployment feed yourself chatbot"
    - "preference based interactive multi document summarisation"

Connected through hyperedge HE-3 (induced by source paper "on the difficulty of warm starting neural network training"):
  with members:
    - "generalization in deep networks the role of distance from initialization"
    - "an analysis of pre training on object detection"
    - "energy and policy considerations for deep learning in nlp"
    - "green ai"
    - "on first order meta learning algorithms"
    - "reptile a scalable metalearning algorithm"
    - "model based active exploration"
    - "fisher rao metric geometry and complexity of neural networks"

Connected through hyperedge HE-4 (induced by source paper "deep active learning unified and principled method for query and training"):
  with members:
    - "bayesian generative active deep learning"
    - "variational adversarial active learning"
    - "a principled approach for learning task similarity in multitask learning"
    - "adversarial active learning for deep networks a margin based approach"
    - "adversarial sampling for active learning"
    - "generative adversarial active learning"
    - "learning algorithms for active learning"
    - "improved training of wasserstein gans"

Connected through hyperedge HE-5 (induced by source paper "adversarial representation active learning"):
  with members:
    - "bayesian generative active deep learning"
    - "large scale adversarial representation learning"
    - "rethinking deep active learning using unlabeled data at model training"
    - "variational adversarial active learning"
    - "high fidelity image generation with fewer labels"
    - "large scale gan training for high fidelity natural image synthesis"
    - "gradient free policy architecture search and adaptation"
    - "generative adversarial active 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)