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: "convex multitask learning with flexible task clusters"
  Abstract: Traditionally, multitask learning (MTL) assumes that all the tasks are related. This can lead to negative transfer when tasks are indeed incoherent. Recently, a number of approaches have been proposed that alleviate this problem by discovering the underlying task clusters or relationships. However, they are limited to modeling these relationships at the task level, which may be restrictive in some applications. In this paper, we propose a novel MTL formulation that captures task relationships at the feature-level. Depending on the interactions among tasks and features, the proposed method construct different task clusters for different features, without even the need of pre-specifying the number of clusters. Computationally, the proposed formulation is strongly convex, and can be efficiently solved by accelerated proximal methods. Experiments are performed on a number of synthetic and real-world data sets. Under various degrees of task relationships, the accuracy of the proposed method is consistently among the best. Moreover, the feature-specific task clusters obtained agree with the known/plausible task structures of the data.

Connected through hyperedge HE-1 (induced by source paper "joint manifold diffusion for combining predictions on decoupled observations"):
  with members:
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "very deep convolutional networks for large scale image recognition"
    - "decaf a deep convolutional activation feature for generic visual recognition"
    - "flexible modeling of latent task structures in multitask learning"
    - "algorithms for learning kernels based on centered alignment"
    - "learning task grouping and overlap in multi task learning"

Connected through hyperedge HE-2 (induced by source paper "a riemannian gossip approach to subspace learning on grassmann manifold"):
  with members:
    - "riemannian stochastic variance reduced gradient algorithm with retraction and vector transport"
    - "a survey on multi task learning"
    - "dimensionality reduction on spd manifolds the emergence of geometry aware methods"
    - "a riemannian gossip approach to decentralized matrix completion"
    - "manopt a matlab toolbox for optimization on manifolds"
    - "a convex formulation for learning task relationships in multi task learning"
    - "fixed rank matrix factorizations and riemannian low rank optimization"
    - "a convex feature learning formulation for latent task structure discovery"

Connected through hyperedge HE-3 (induced by source paper "convex learning of multiple tasks and their structure"):
  with members:
    - "learning output kernels for multi task problems"
    - "a convex formulation for learning task relationships in multi task learning"
    - "learning task grouping and overlap in multi task learning"
    - "trace lasso a trace norm regularization for correlated designs"
    - "clustered multi task learning a convex formulation"

Connected through hyperedge HE-4 (induced by source paper "learning multiple visual tasks while discovering their structure"):
  with members:
    - "decaf a deep convolutional activation feature for generic visual recognition"
    - "a convex formulation for learning task relationships in multi task learning"
    - "learning task grouping and overlap in multi task learning"
    - "clustered multi task learning a convex formulation"

Connected through hyperedge HE-5 (induced by source paper "multi task learning with group specific feature space sharing"):
  with members:
    - "pareto path multitask multiple kernel learning"
    - "conic multi task classification"
    - "a convex formulation for learning task relationships in multi task learning"
    - "clustered multi task learning a convex formulation"

Connected through hyperedge HE-6 (induced by source paper "conic multi task classification"):
  with members:
    - "pareto path multitask multiple kernel learning"
    - "a convex formulation for learning task relationships in multi task learning"
    - "structured sparsity and generalization"
    - "regularization techniques for learning with matrices"

Connected through hyperedge HE-7 (induced by source paper "sparse coding for multitask and transfer learning"):
  with members:
    - "flexible modeling of latent task structures in multitask learning"
    - "learning task grouping and overlap in multi task learning"
    - "a model of inductive bias learning"
    - "learning to learn"

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)