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: "distributed multi task relationship learning"
  Abstract: In this paper, we propose a distributed multi-task learning framework that simultaneously learns predictive models for each task as well as task relationships between tasks alternatingly in the parameter server paradigm. In our framework, we first offer a general dual form for a family of regularized multi-task relationship learning methods. Subsequently, we propose a communication-efficient primal-dual distributed optimization algorithm to solve the dual problem by carefully designing local subproblems to make the dual problem decomposable. Moreover, we provide a theoretical convergence analysis for the proposed algorithm, which is specific for distributed multi-task relationship learning. We conduct extensive experiments on both synthetic and real-world datasets to evaluate our proposed framework in terms of scalability, effectiveness, and convergence.

Connected through hyperedge HE-1 (induced by source paper "branched multi task networks deciding what layers to share"):
  with members:
    - "stochastic filter groups for multi task cnns learning specialist and generalist convolution kernels"
    - "representation similarity analysis for efficient task taxonomy transfer learning"
    - "feature partitioning for efficient multi task architectures"
    - "attentive single tasking of multiple tasks"
    - "a modulation module for multi task learning with applications in image retrieval"
    - "evolutionary architecture search for deep multitask networks"
    - "regularized evolution for image classifier architecture search"
    - "taskonomy disentangling task transfer learning"

Connected through hyperedge HE-2 (induced by source paper "zero shot task transfer"):
  with members:
    - "layoutnet reconstructing the 3d room layout from a single rgb image"
    - "latent ransac"
    - "bayesian model agnostic meta learning"
    - "taskonomy disentangling task transfer learning"
    - "adversarial data programming using gans to relax the bottleneck of curated labeled data"
    - "domain adaptation for visual applications a comprehensive survey"
    - "maximum classifier discrepancy for unsupervised domain adaptation"
    - "zero shot task generalization with multi task deep reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "learning task relatedness in multi task learning for images in context"):
  with members:
    - "taskonomy disentangling task transfer learning"
    - "gnas a greedy neural architecture search method for multi attribute learning"
    - "sketchparse towards rich descriptions for poorly drawn sketches using multi task hierarchical deep networks"
    - "an overview of multi task learning in deep neural networks"
    - "omniart multi task deep learning for artistic data analysis"
    - "deep cross residual learning for multitask visual recognition"
    - "deep multi task representation learning a tensor factorisation approach"
    - "ubernet training a universal convolutional neural network for low mid and high level vision using diverse datasets and limited memory"

Connected through hyperedge HE-4 (induced by source paper "decentralized multi task learning based on extreme learning machines"):
  with members:
    - "on better exploring and exploiting task relationships in multitask learning joint model and feature learning"
    - "federated multi task learning"
    - "proximal multitask learning over networks with sparsity inducing coregularization"
    - "distributed multi task learning with shared representation"
    - "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 "federated multi task learning"):
  with members:
    - "federated learning strategies for improving communication efficiency"
    - "cocoa a general framework for communication efficient distributed optimization"
    - "communication efficient learning of deep networks from decentralized data"
    - "xnor net imagenet classification using binary convolutional neural networks"
    - "asynchronous multi task learning"
    - "distributed multi task learning with shared representation"
    - "tensor switching networks"
    - "convexified convolutional neural networks"

Connected through hyperedge HE-6 (induced by source paper "an overview of multi task learning in deep neural networks"):
  with members:
    - "deep voice real time neural text to speech"
    - "semi supervised multitask learning for sequence labeling"
    - "deep multi task representation learning a tensor factorisation approach"
    - "cross stitch networks for multi task learning"
    - "when is multitask learning effective semantic sequence prediction under varying data conditions"
    - "trace norm regularised deep multi task learning"
    - "a joint many task model growing a neural network for multiple nlp tasks"
    - "fast r cnn"

Connected through hyperedge HE-7 (induced by source paper "multi agent distributed lifelong learning for collective knowledge acquisition"):
  with members:
    - "asynchronous multi task learning"
    - "multitask diffusion adaptation over networks"
    - "sparse coding for multitask and transfer learning"
    - "learning task grouping and overlap in multi task learning"
    - "distributed autonomous online learning regrets and intrinsic privacy preserving properties"

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)