human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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%.

Connected through hyperedge HE-1 (induced by source paper "uncertainty gated stochastic sequential model for ehr mortality prediction"):
  with members:
    - "on the variance of the adaptive learning rate and beyond"
    - "brits bidirectional recurrent imputation for time series"
    - "handling incomplete heterogeneous data using vaes"
    - "focal loss for dense object detection"
    - "estimating missing data in temporal data streams using multi directional recurrent neural networks"
    - "medical concept representation learning from electronic health records and its application on heart failure prediction"
    - "structured prediction models for rnn based sequence labeling in clinical text"
    - "recurrent neural networks for multivariate time series with missing values"

Connected through hyperedge HE-2 (induced by source paper "discovering invariances in healthcare neural networks"):
  with members:
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "mimic extract a data extraction preprocessing and representation pipeline for mimic iii"
    - "on the convergence of adam and beyond"
    - "finding invariants in deep neural networks"
    - "exploiting excessive invariance caused by norm bounded adversarial robustness"
    - "pytorch an imperative style high performance deep learning library"
    - "excessive invariance causes adversarial vulnerability"
    - "scalable and accurate deep learning for electronic health records"

Connected through hyperedge HE-3 (induced by source paper "explaining deep classification of time series data with learned prototypes"):
  with members:
    - "deep learning for time series classification a review"
    - "this looks like that deep learning for interpretable image recognition"
    - "deep learning for case based reasoning through prototypes a neural network that explains its predictions"
    - "end to end learning for music audio tagging at scale"
    - "why should i trust you explaining the predictions of any classifier"
    - "wavenet a generative model for raw audio"
    - "learning deep features for discriminative localization"

Question: Please tell me which class the center node 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)