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: "deep multi task representation learning a tensor factorisation approach"
  Abstract: Most contemporary multi-task learning methods assume linear models. This setting is considered shallow in the era of deep learning. In this paper, we present a new deep multi-task representation learning framework that learns cross-task sharing structure at every layer in a deep network. Our approach is based on generalising the matrix factorisation techniques explicitly or implicitly used by many conventional MTL algorithms to tensor factorisation, to realise automatic learning of end-to-end knowledge sharing in deep networks. This is in contrast to existing deep learning approaches that need a user-defined multi-task sharing strategy. Our approach applies to both homogeneous and heterogeneous MTL. Experiments demonstrate the efficacy of our deep multi-task representation learning in terms of both higher accuracy and fewer design choices.

Connected through hyperedge HE-1 (induced by source paper "tensor decompositions in deep learning"):
  with members:
    - "theoretically expressive and edge aware graph learning"
    - "tucker tensor layer in fully connected neural networks"
    - "a fair comparison of graph neural networks for graph classification"
    - "bayesian tensor factorisation for bottom up hidden tree markov models"
    - "a gentle introduction to deep learning for graphs"
    - "contextual graph markov model a deep and generative approach to graph processing"
    - "how powerful are graph neural networks"
    - "mutan multimodal tucker fusion for visual question answering"

Connected through hyperedge HE-2 (induced by source paper "unified multi domain learning and data imputation using adversarial autoencoder"):
  with members:
    - "gain missing data imputation using generative adversarial nets"
    - "handling incomplete heterogeneous data using vaes"
    - "evolutionary architecture search for deep multitask networks"
    - "taking a closer look at domain shift category level adversaries for semantics consistent domain adaptation"
    - "cycada cycle consistent adversarial domain adaptation"
    - "unified deep supervised domain adaptation and generalization"
    - "vae with a vampprior"
    - "a survey on multi task learning"

Connected through hyperedge HE-3 (induced by source paper "a simple general approach to balance task difficulty in multi task learning"):
  with members:
    - "pareto multi task learning"
    - "multi task learning as multi objective optimization"
    - "deep hashing network for unsupervised domain adaptation"
    - "adversarial multi task learning for text classification"
    - "gradnorm gradient normalization for adaptive loss balancing in deep multitask networks"
    - "trace norm regularised deep multi task learning"
    - "learning multiple tasks with multilinear relationship networks"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "deep multi task augmented feature learning via hierarchical graph neural network"):
  with members:
    - "graph star net for generalized multi task learning"
    - "multi task learning over graph structures"
    - "learning to multitask"
    - "trace norm regularised deep multi task learning"
    - "multi domain dialog state tracking using recurrent neural networks"
    - "heterogeneous multi task learning for human pose estimation with deep convolutional neural network"
    - "a convex formulation for learning task relationships in multi task learning"
    - "learning task grouping and overlap in multi task learning"

Connected through hyperedge HE-5 (induced by source paper "deep multi task learning via generalized tensor trace norm"):
  with members:
    - "learning to multitask"
    - "trace norm regularised deep multi task learning"
    - "learning multiple tasks with multilinear relationship networks"
    - "very deep convolutional networks for large scale image recognition"
    - "a convex formulation for learning task relationships in multi task learning"
    - "learning task grouping and overlap in multi task learning"

Connected through hyperedge HE-6 (induced by source paper "auxiliary learning for deep multi task learning"):
  with members:
    - "on the optimization of deep networks implicit acceleration by overparameterization"
    - "deep attention based classification network for robust depth prediction"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "ocnet object context network for scene parsing"
    - "deep ordinal regression network for monocular depth estimation"
    - "a hierarchical multi task approach for learning embeddings from semantic tasks"
    - "discrimination aware channel pruning for deep neural networks"

Connected through hyperedge HE-7 (induced by source paper "transferability and hardness of supervised classification tasks"):
  with members:
    - "registration free face ssd single shot analysis of smiles facial attributes and affect in the wild"
    - "beyond sharing weights for deep domain adaptation"
    - "task2vec task embedding for meta learning"
    - "on measuring the iconicity of a face"
    - "the information complexity of learning tasks their structure and their distance"
    - "toward understanding catastrophic forgetting in continual learning"
    - "regularized learning for domain adaptation under label shifts"
    - "meta learning with latent embedding optimization"

Connected through hyperedge HE-8 (induced by source paper "t net parametrizing fully convolutional nets with a single high order tensor"):
  with members:
    - "quantized densely connected u nets for efficient landmark localization"
    - "on the power of over parametrization in neural networks with quadratic activation"
    - "cp decomposition with tensor power method for convolutional neural networks compression"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "tensor contraction layers for parsimonious deep nets"
    - "tensorizing generative adversarial nets"
    - "binarized convolutional landmark localizers for human pose estimation and face alignment with limited resources"
    - "mask r cnn"

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)