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 "temporal clustering invariance in irregular healthcare time series"):
  Abstract: Electronic records contain sequences of events, some of which take place all at once in a single visit, and others that are dispersed over multiple visits, each with a different timestamp. We postulate that fine temporal detail, e.g., whether a series of blood tests are completed at once or in rapid succession should not alter predictions based on this data. Motivated by this intuition, we propose models for analyzing sequences of multivariate clinical time series data that are invariant to this temporal clustering. We propose an efficient data augmentation technique that exploits the postulated temporal-clustering invariance to regularize deep neural networks optimized for several clinical prediction tasks. We introduce two techniques to temporally coarsen (downsample) irregular time series: (i) grouping the data points based on regularly-spaced timestamps; and (ii) clustering them, yielding irregularly-paced timestamps. Moreover, we propose a MultiResolution Ensemble (MRE) model, improving predictive accuracy by ensembling predictions based on inputs sequences transformed by different coarsening operators. Our experiments show that MRE improves the mAP on the benchmark mortality prediction task from 51.53% to 53.92%.

Includes members:
  Member "on the convergence of adam and beyond":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "disentangling adaptive gradient methods from learning rates")
  Member "interpolation prediction networks for irregularly sampled time series":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "adaptive prediction timing for electronic health records")
  Member "learning to exploit invariances in clinical time series data using sequence transformer networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "bmi a behavior measurement indicator for fuel poverty using aggregated load readings from smart meters")
  Member "dipole diagnosis prediction in healthcare via attention based bidirectional recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "zimm a deep learning model for long term and blurry relapses with non clinical claims data")
  Member "learning from clinical judgments semi markov modulated marked hawkes processes for risk prognosis":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning multivariate hawkes processes at scale")
  Member "modeling missing data in clinical time series with rnns":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "why not to use zero imputation correcting sparsity bias in training neural networks")
  Member "group equivariant convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a permutation equivariant neural network architecture for auction design")
  Member "recurrent neural networks for multivariate time series with missing values":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "highres net recursive fusion for multi frame super resolution of satellite imagery")

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)