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: "dusk a dual structure preserving kernel for supervised tensor learning with applications to neuroimages"
  Abstract: With advances in data collection technologies, tensor data is assuming increasing prominence in many applications and the problem of supervised tensor learning has emerged as a topic of critical significance in the data mining and machine learning community. Conventional methods for supervised tensor learning mainly focus on learning kernels by flattening the tensor into vectors or matrices, however structural information within the tensors will be lost. In this paper, we introduce a new scheme to design structure-preserving kernels for supervised tensor learning. Specifically, we demonstrate how to leverage the naturally available structure within the tensorial representation to encode prior knowledge in the kernel. We proposed a tensor kernel that can preserve tensor structures based upon dual-tensorial mapping. The dual-tensorial mapping function can map each tensor instance in the input space to another tensor in the feature space while preserving the tensorial structure. Theoretically, our approach is an extension of the conventional kernels in the vector space to tensor space. We applied our novel kernel in conjunction with SVM to real-world tensor classification problems including brain fMRI classification for three different diseases (i.e., Alzheimer's disease, ADHD and brain damage by HIV). Extensive empirical studies demonstrate that our proposed approach can effectively boost tensor classification performances, particularly with small sample sizes.

Connected through hyperedge HE-1 (induced by source paper "efficient structure preserving support tensor train machine"):
  with members:
    - "kernelized support tensor train machines"
    - "a support tensor train machine"
    - "tensor networks for dimensionality reduction and large scale optimization part 1 low rank tensor decompositions"
    - "speeding up convolutional neural networks using fine tuned cp decomposition"
    - "tensor decompositions a new concept in brain data analysis"
    - "learning tensors in reproducing kernel hilbert spaces with multilinear spectral penalties"

Connected through hyperedge HE-2 (induced by source paper "kernelized support tensor train machines"):
  with members:
    - "a support tensor train machine"
    - "parallelized tensor train learning of polynomial classifiers"
    - "tensorizing neural networks"
    - "speeding up convolutional neural networks using fine tuned cp decomposition"

Connected through hyperedge HE-3 (induced by source paper "learning from multi view multi way data via structural factorization machines"):
  with members:
    - "neural factorization machines for sparse predictive analytics"
    - "deep embedding forest forest based serving with deep embedding features"
    - "deepmood modeling mobile phone typing dynamics for mood detection"
    - "deep cross network for ad click predictions"
    - "a broad learning approach for context aware mobile application recommendation"
    - "joint deep modeling of users and items using reviews for recommendation"
    - "deepfm a factorization machine based neural network for ctr prediction"
    - "attentional factorization machines learning the weight of feature interactions via attention networks"

Connected through hyperedge HE-4 (induced by source paper "contaminant removal for android malware detection systems"):
  with members:
    - "sequential keystroke behavioral biometrics for mobile user identification via multi view deep learning"
    - "multi view unsupervised feature selection by cross diffused matrix alignment"
    - "joint deep modeling of users and items using reviews for recommendation"

Connected through hyperedge HE-5 (induced by source paper "sequential keystroke behavioral biometrics for mobile user identification via multi view deep learning"):
  with members:
    - "an overview of gradient descent optimization algorithms"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"

Connected through hyperedge HE-6 (induced by source paper "a review of heterogeneous data mining for brain disorders"):
  with members:
    - "multi view machines"
    - "tensor decompositions for signal processing applications from two way to multiway component analysis"
    - "mining brain networks using multiple side views for neurological disorder identification"
    - "a survey on multi view learning"
    - "higher order partial least squares hopls a generalized multilinear regression method"
    - "discriminative feature selection for uncertain graph classification"

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)