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: "sparse hierarchical tucker factorization and its application to healthcare"
  Abstract: We propose a new tensor factorization method, called the Sparse Hierarchical-Tucker (Sparse H-Tucker), for sparse and high-order data tensors. Sparse H-Tucker is inspired by its namesake, the classical Hierarchical Tucker method, which aims to compute a tree-structured factorization of an input data set that may be readily interpreted by a domain expert. However, Sparse H-Tucker uses a nested sampling technique to overcome a key scalability problem in Hierarchical Tucker, which is the creation of an unwieldy intermediate dense core tensor; the result of our approach is a faster, more space-efficient, and more accurate method. We extensively test our method on a real healthcare dataset, which is collected from 30K patients and results in an 18th order sparse data tensor. Unlike competing methods, Sparse H-Tucker can analyze the full data set on a single multi-threaded machine. It can also do so more accurately and in less time than the state-of-the-art: on a 12th order subset of the input data, Sparse H-Tucker is 18x more accurate and 7.5x faster than a previously state-of-the-art method. Even for analyzing low order tensors (e.g., 4-order), our method requires close to an order of magnitude less time and over two orders of magnitude less memory, as compared to traditional tensor factorization methods such as CP and Tucker. Moreover, we observe that Sparse H-Tucker scales nearly linearly in the number of non-zero tensor elements. The resulting model also provides an interpretable disease hierarchy, which is confirmed by a clinical expert.

Connected through hyperedge HE-1 (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"
    - "spartan scalable parafac2 for large sparse data"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "compositional hierarchical tensor factorization representing hierarchical intrinsic and extrinsic causal factors"):
  with members:
    - "tensor contraction layers for parsimonious deep nets"
    - "convolutional rectifier networks as generalized tensor decompositions"
    - "an end to end system for unconstrained face verification with deep convolutional neural networks"
    - "tensorizing neural networks"
    - "compression of deep convolutional neural networks for fast and low power mobile applications"
    - "on the expressive power of deep learning a tensor analysis"
    - "theano new features and speed improvements"

Connected through hyperedge HE-3 (induced by source paper "optimizing the linear fascicle evaluation algorithm for multi core and many core systems"):
  with members:
    - "design principles for sparse matrix multiplication on the gpu"
    - "on optimizing distributed tucker decomposition for dense tensors"
    - "tensor decompositions for signal processing applications from two way to multiway component analysis"

Connected through hyperedge HE-4 (induced by source paper "sustain scalable unsupervised scoring for tensors and its application to phenotyping"):
  with members:
    - "spartan scalable parafac2 for large sparse data"
    - "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-5 (induced by source paper "ctd fast accurate and interpretable method for static and dynamic tensor decompositions"):
  with members:
    - "identifying the signs of fraudulent accounts using data mining techniques"
    - "relative error cur matrix decompositions"

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)