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: "resset a recurrent model for sequence of sets with applications to electronic medical records"
  Abstract: Modern healthcare is ripe for disruption by AI. A game changer would be automatic understanding the latent processes from electronic medical records, which are being collected for billions of people worldwide. However, these healthcare processes are complicated by the interaction between at least three dynamic components: the illness which involves multiple diseases, the care which involves multiple treatments, and the recording practice which is biased and erroneous. Existing methods are inadequate in capturing the dynamic structure of care. We propose Resset, an end-to-end recurrent model that reads medical record and predicts future risk. The model adopts the algebraic view in that discrete medical objects are embedded into continuous vectors lying in the same space. We formulate the problem as modeling sequences of sets, a novel setting that have rarely, if not, been addressed. Within Resset, the bag of diseases recorded at each clinic visit is modeled as function of sets. The same hold for the bag of treatments. The interaction between the disease bag and the treatment bag at a visit is modeled in several, one of which as residual of diseases minus the treatments. Finally, the health trajectory, which is a sequence of visits, is modeled using a recurrent neural network. We report results on over a hundred thousand hospital visits by patients suffered from two costly chronic diseases -- diabetes and mental health. Resset shows promises in multiple predictive tasks such as readmission prediction, treatments recommendation and diseases progression.

Connected through hyperedge HE-1 (induced by source paper "graph convolutional transformer learning the graphical structure of electronic health records"):
  with members:
    - "pre training of graph augmented transformers for medication recommendation"
    - "behrt transformer for electronic health records"
    - "inpatient2vec medical representation learning for inpatients"
    - "mime multilevel medical embedding of electronic health records for predictive healthcare"
    - "deep sets"
    - "relational inductive biases deep learning and graph networks"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "how powerful are graph neural networks"

Connected through hyperedge HE-2 (induced by source paper "learning the graphical structure of electronic health records with graph convolutional transformer"):
  with members:
    - "mime multilevel medical embedding of electronic health records for predictive healthcare"
    - "relational inductive biases deep learning and graph networks"
    - "how powerful are graph neural networks"
    - "scalable and accurate deep learning for electronic health records"
    - "clinical intervention prediction and understanding using deep networks"
    - "non local neural networks"
    - "attention is all you need"
    - "dipole diagnosis prediction in healthcare via attention based bidirectional recurrent neural networks"

Connected through hyperedge HE-3 (induced by source paper "benchmarking deep learning architectures for predicting readmission to the icu and describing patients at risk"):
  with members:
    - "latent odes for irregularly sampled time series"
    - "predicting cardiovascular risk factors in retinal fundus photographs using deep learning"
    - "medical concept embedding with time aware attention"
    - "deep ehr chronic disease prediction using medical notes"
    - "scalable and accurate deep learning for electronic health records"
    - "discrete event continuous time rnns"
    - "recurrent neural networks for multivariate time series with missing values"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "mime multilevel medical embedding of electronic health records for predictive healthcare"):
  with members:
    - "multi task learning for mental health using social media text"
    - "hadamard product for low rank bilinear pooling"
    - "gram graph based attention model for healthcare representation learning"
    - "retain interpretable predictive model in healthcare using reverse time attention mechanism"
    - "multimodal compact bilinear pooling for visual question answering and visual grounding"
    - "compact bilinear pooling"
    - "learning to diagnose with lstm recurrent neural networks"
    - "adam a method for stochastic optimization"

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.NE(Neural and Evolutionary Computing)