human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "information condensing active learning"):
  Abstract: We introduce Information Condensing Active Learning (ICAL), a batch mode model agnostic Active Learning (AL) method targeted at Deep Bayesian Active Learning that focuses on acquiring labels for points which have as much information as possible about the still unacquired points. ICAL uses the Hilbert Schmidt Independence Criterion (HSIC) to measure the strength of the dependency between a candidate batch of points and the unlabeled set. We develop key optimizations that allow us to scale our method to large unlabeled sets. We show significant improvements in terms of model accuracy and negative log likelihood (NLL) on several image datasets compared to state of the art batch mode AL methods for deep learning.

Includes members:
  Member "batchbald efficient and diverse batch acquisition for deep bayesian active learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "task aware variational adversarial active learning")
  Member "understanding the limitations of variational mutual information estimators":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "conditional mutual information based contrastive loss for financial time series forecasting")
  Member "deep batch active learning by diverse uncertain gradient lower bounds":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "fine tuning language models from human preferences")
  Member "adversarial active learning for deep networks a margin based approach":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a framework for the extraction of deep neural networks by leveraging public data")
  Member "discriminative active learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "fine tuning language models from human preferences")
  Member "emnist an extension of mnist to handwritten letters":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "anomalous instance detection in deep learning a survey")
  Member "deep bayesian active learning with image data":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "action for better prediction")
  Member "deep active learning over the long tail":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "active learning for entity alignment")

Question: Please tell me which class the source paper 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)