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: "spartan scalable parafac2 for large sparse data"
  Abstract: In exploratory tensor mining, a common problem is how to analyze a set of variables across a set of subjects whose observations do not align naturally. For example, when modeling medical features across a set of patients, the number and duration of treatments may vary widely in time, meaning there is no meaningful way to align their clinical records across time points for analysis purposes. To handle such data, the state-of-the-art tensor model is the so-called PARAFAC2, which yields interpretable and robust output and can naturally handle sparse data. However, its main limitation up to now has been the lack of efficient algorithms that can handle large-scale datasets. #R##N#In this work, we fill this gap by developing a scalable method to compute the PARAFAC2 decomposition of large and sparse datasets, called SPARTan. Our method exploits special structure within PARAFAC2, leading to a novel algorithmic reformulation that is both fast (in absolute time) and more memory-efficient than prior work. We evaluate SPARTan on both synthetic and real datasets, showing 22X performance gains over the best previous implementation and also handling larger problem instances for which the baseline fails. Furthermore, we are able to apply SPARTan to the mining of temporally-evolving phenotypes on data taken from real and medically complex pediatric patients. The clinical meaningfulness of the phenotypes identified in this process, as well as their temporal evolution over time for several patients, have been endorsed by clinical experts.

Connected through hyperedge HE-1 (induced by source paper "a parallel sparse tensor benchmark suite on cpus and gpus"):
  with members:
    - "pasta a parallel sparse tensor algorithm benchmark suite"
    - "format abstraction for sparse tensor algebracompilers"
    - "a unified optimization approach for sparse tensor operations on gpus"
    - "simtensor a synthetic tensor data generator"
    - "high performance tensor contraction without transposition"
    - "tensorizing neural networks"
    - "the gap benchmark suite"
    - "speeding up convolutional neural networks using fine tuned cp decomposition"

Connected through hyperedge HE-2 (induced by source paper "pasta a parallel sparse tensor algorithm benchmark suite"):
  with members:
    - "load balanced sparse mttkrp on gpus"
    - "tensor train decomposition on tensorflow t3f"
    - "a practical randomized cp tensor decomposition"
    - "software for sparse tensor decomposition on emerging computing architectures"
    - "tensor regression meets gaussian processes"
    - "tensor computation a new framework for high dimensional problems in eda"
    - "a unified optimization approach for sparse tensor operations on gpus"
    - "learning from multiway data simple and efficient tensor regression"

Connected through hyperedge HE-3 (induced by source paper "machine learning and visualization in clinical decision support current state and future directions"):
  with members:
    - "deep ehr a survey of recent advances in deep learning techniques for electronic health record ehr analysis"
    - "generating multi label discrete patient records using generative adversarial networks"
    - "continuous state space models for optimal sepsis treatment a deep reinforcement learning approach"
    - "representation and reinforcement learning for personalized glycemic control in septic patients"
    - "improving palliative care with deep learning"
    - "the use of autoencoders for discovering patient phenotypes"
    - "dipole diagnosis prediction in healthcare via attention based bidirectional recurrent neural networks"
    - "prediction constrained topic models for antidepressant recommendation"

Connected through hyperedge HE-4 (induced by source paper "taste temporal and static tensor factorization for phenotyping electronic health records"):
  with members:
    - "copa constrained parafac2 for sparse large datasets"
    - "sustain scalable unsupervised scoring for tensors and its application to phenotyping"

Connected through hyperedge HE-5 (induced by source paper "sustain scalable unsupervised scoring for tensors and its application to phenotyping"):
  with members:
    - "sparse hierarchical tucker factorization and its application to healthcare"
    - "mpi faun an mpi based framework for alternating updating nonnegative matrix factorization"
    - "column reordering for box constrained integer least squares problems"

Connected through hyperedge HE-6 (induced by source paper "copa constrained parafac2 for sparse large datasets"):
  with members:
    - "federated tensor factorization for computational phenotyping"

Connected through hyperedge HE-7 (induced by source paper "mimis minimally intrusive mining of smartphone user behaviors"):
  with members:
    - "discovering hidden structure in high dimensional human behavioral data via tensor factorization"

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)