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 "a hidden absorbing semi markov model for informatively censored temporal data learning and inference"):
  Abstract: Modeling continuous-time physiological processes that manifest a patient's evolving clinical states is a key step in approaching many problems in healthcare. In this paper, we develop the Hidden Absorbing Semi-Markov Model (HASMM): a versatile probabilistic model that is capable of capturing the modern electronic health record (EHR) data. Unlike exist- ing models, an HASMM accommodates irregularly sampled, temporally correlated, and informatively censored physiological data, and can describe non-stationary clinical state transitions. Learning an HASMM from the EHR data is achieved via a novel forward- filtering backward-sampling Monte-Carlo EM algorithm that exploits the knowledge of the end-point clinical outcomes (informative censoring) in the EHR data, and implements the E-step by sequentially sampling the patients' clinical states in the reverse-time direction while conditioning on the future states. Real-time inferences are drawn via a forward- filtering algorithm that operates on a virtually constructed discrete-time embedded Markov chain that mirrors the patient's continuous-time state trajectory. We demonstrate the di- agnostic and prognostic utility of the HASMM in a critical care prognosis setting using a real-world dataset for patients admitted to the Ronald Reagan UCLA Medical Center.

Includes members:
  Member "learning from clinical judgments semi markov modulated marked hawkes processes for risk prognosis":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning multivariate hawkes processes at scale")
  Member "directly modeling missing data in sequences with rnns improved classification of clinical time series":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a deep learning approach to diagnosing multiple sclerosis from smartphone data")
  Member "personalized risk scoring for critical care prognosis using mixtures of gaussian processes":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multi task time series analysis applied to drug response modelling")
  Member "input output non linear dynamical systems applied to physiological condition monitoring":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multi task time series analysis applied to drug response modelling")
  Member "balancing suspense and surprise timely decision making with endogenous information acquisition":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "asac active sensing using actor critic models")
  Member "recurrent neural networks for multivariate time series with missing values":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "highres net recursive fusion for multi frame super resolution of satellite imagery")
  Member "expectation maximization and complex duration distributions for continuous time bayesian networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "rankmap a platform aware framework for distributed learning from dense datasets")

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.AI(Artificial Intelligence)