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: "learning to exploit invariances in clinical time series data using sequence transformer networks"
  Abstract: Recently, researchers have started applying convolutional neural networks (CNNs) with one-dimensional convolutions to clinical tasks involving time-series data. This is due, in part, to their computational efficiency, relative to recurrent neural networks and their ability to efficiently exploit certain temporal invariances, (e.g., phase invariance). However, it is well-established that clinical data may exhibit many other types of invariances (e.g., scaling). While preprocessing techniques, (e.g., dynamic time warping) may successfully transform and align inputs, their use often requires one to identify the types of invariances in advance. In contrast, we propose the use of Sequence Transformer Networks, an end-to-end trainable architecture that learns to identify and account for invariances in clinical time-series data. Applied to the task of predicting in-hospital mortality, our proposed approach achieves an improvement in the area under the receiver operating characteristic curve (AUROC) relative to a baseline CNN (AUROC=0.851 vs. AUROC=0.838). Our results suggest that a variety of valuable invariances can be learned directly from the data.

Connected through hyperedge HE-1 (induced by source paper "bmi a behavior measurement indicator for fuel poverty using aggregated load readings from smart meters"):
  with members:
    - "detecting activities of daily living and routine behaviours in dementia patients living alone using smart meter load disaggregation"
    - "big data analytics for dynamic energy management in smart grids"

Connected through hyperedge HE-2 (induced by source paper "multi task self supervised learning for human activity detection"):
  with members:
    - "measuring the intrinsic dimension of objective landscapes"
    - "deep learning for sensor based activity recognition a survey"
    - "unsupervised representation learning by predicting image rotations"
    - "universal language model fine tuning for text classification"
    - "protecting sensory data against sensitive inferences"
    - "cooperative learning of audio and video models from self supervised synchronization"
    - "realistic evaluation of deep semi supervised learning algorithms"
    - "audio visual scene analysis with self supervised multisensory features"

Connected through hyperedge HE-3 (induced by source paper "temporal transformer networks joint learning of invariant and discriminative time warping"):
  with members:
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "can recurrent neural networks warp time"
    - "interpretable 3d human action analysis with temporal convolutional networks"
    - "dynamic routing between capsules"
    - "learning invariant riemannian geometric representations using deep nets"
    - "graph based isometry invariant representation learning"
    - "a new representation of skeleton sequences for 3d action recognition"
    - "view adaptive recurrent neural networks for high performance human action recognition from skeleton data"

Connected through hyperedge HE-4 (induced by source paper "temporal clustering invariance in irregular healthcare time series"):
  with members:
    - "on the convergence of adam and beyond"
    - "interpolation prediction networks for irregularly sampled time series"
    - "dipole diagnosis prediction in healthcare via attention based bidirectional recurrent neural networks"
    - "learning from clinical judgments semi markov modulated marked hawkes processes for risk prognosis"
    - "modeling missing data in clinical time series with rnns"
    - "group equivariant convolutional networks"
    - "recurrent neural networks for multivariate time series with missing values"
    - "beyond short snippets deep networks for video classification"

Connected through hyperedge HE-5 (induced by source paper "modelling segmented cardiotocography time series signals using one dimensional convolutional neural networks for the early detection of abnormal birth outcomes"):
  with members:
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "smote synthetic minority over sampling technique"

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)