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 "mgp atttcn an interpretable machine learning model for the prediction of sepsis"):
  Abstract: With a mortality rate of 5.4 million lives worldwide every year and a healthcare cost of more than 16 billion dollars in the USA alone, sepsis is one of the leading causes of hospital mortality and an increasing concern in the ageing western world. Recently, medical and technological advances have helped re-define the illness criteria of this disease, which is otherwise poorly understood by the medical society. Together with the rise of widely accessible Electronic Health Records, the advances in data mining and complex nonlinear algorithms are a promising avenue for the early detection of sepsis. This work contributes to the research effort in the field of automated sepsis detection with an open-access labelling of the medical MIMIC-III data set. Moreover, we propose MGP-AttTCN: a joint multitask Gaussian Process and attention-based deep learning model to early predict the occurrence of sepsis in an interpretable manner. We show that our model outperforms the current state-of-the-art and present evidence that different labelling heuristics lead to discrepancies in task difficulty.

Includes members:
  Member "early recognition of sepsis with gaussian process temporal convolutional networks and dynamic time warping":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "explainable artificial intelligence model to predict acute critical illness from electronic health records")
  Member "medical time series classification with hierarchical attention based temporal convolutional networks a case study of myotonic dystrophy diagnosis":
    also connected through other hyperedges:
      (none)
  Member "model based reinforcement learning for sepsis treatment":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "reinforcement learning in healthcare a survey")
  Member "mattnet modular attention network for referring expression comprehension":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "relationship embedded representation learning for grounding referring expressions")
  Member "attention gated networks learning to leverage salient regions in medical images":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "an interpretable classifier for high resolution breast cancer screening images utilizing weakly supervised localization")
  Member "som vae interpretable discrete representation learning on time series":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "are disentangled representations helpful for abstract visual reasoning")
  Member "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "blank language models")
  Member "a dual stage attention based recurrent neural network for time series prediction":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "conditional mutual information based contrastive loss for financial time series forecasting")

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)