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 "explaining deep classification of time series data with learned prototypes"):
  Abstract: The emergence of deep learning networks raises a need for algorithms to explain their decisions so that users and domain experts can be confident using algorithmic recommendations for high-risk decisions. In this paper we leverage the information-rich latent space induced by such models to learn data representations or prototypes within such networks to elucidate their internal decision-making process. We introduce a novel application of case-based reasoning using prototypes to understand the decisions leading to the classification of time-series data, specifically investigating electrocardiogram (ECG) waveforms for classification of bradycardia, a slowing of heart rate, in infants. We improve upon existing models by explicitly optimizing for increased prototype diversity which in turn improves model accuracy by learning regions of the latent space that highlight features for distinguishing classes. We evaluate the hyperparameter space of our model to show robustness in diversity prototype generation and additionally, explore the resultant latent space of a deep classification network on ECG waveforms via an interactive tool to visualize the learned prototypical waveforms therein. We show that the prototypes are capable of learning real-world features - in our case-study ECG morphology related to bradycardia - as well as features within sub-classes. Our novel work leverages learned prototypical framework on two dimensional time-series data to produce explainable insights during classification tasks.

Includes members:
  Member "temporal clustering invariance in irregular healthcare time series":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "uncertainty gated stochastic sequential model for ehr mortality prediction")
  Member "deep learning for time series classification a review":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "identity recognition in intelligent cars with behavioral data and lstm resnet classifier")
  Member "this looks like that deep learning for interpretable image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "concept whitening for interpretable image recognition")
  Member "deep learning for case based reasoning through prototypes a neural network that explains its predictions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "clara clinical report auto completion")
  Member "end to end learning for music audio tagging at scale":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "toward interpretable music tagging with self attention")
  Member "why should i trust you explaining the predictions of any classifier":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "secure and robust machine learning for healthcare a survey")
  Member "wavenet a generative model for raw audio":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "noise estimation using density estimation for self supervised multimodal learning")
  Member "learning deep features for discriminative localization":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "ha ccn hierarchical attention based crowd counting network")

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)