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 coding for multitask and transfer learning"
  Abstract: We investigate the use of sparse coding and dictionary learning in the context of multitask and transfer learning. The central assumption of our learning method is that the tasks parameters are well approximated by sparse linear combinations of the atoms of a dictionary on a high or infinite dimensional space. This assumption, together with the large quantity of available data in the multitask and transfer learning settings, allows a principled choice of the dictionary. We provide bounds on the generalization error of this approach, for both settings. Numerical experiments on one synthetic and two real datasets show the advantage of our method over single task learning, a previous method based on orthogonal and dense representation of the tasks and a related method learning task grouping.

Connected through hyperedge HE-1 (induced by source paper "learning to learn stochastic gradient descent with biased regularization"):
  with members:
    - "surrogate losses for online learning of stepsizes in stochastic non convex optimization"
    - "introduction to online convex optimization"
    - "fast rates by transferring from auxiliary hypotheses"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "a pac approach to application specific algorithm selection"
    - "a simpler approach to obtaining an o 1 t convergence rate for the projected stochastic subgradient method"
    - "stochastic gradient descent for non smooth optimization convergence results and optimal averaging schemes"
    - "making gradient descent optimal for strongly convex stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "does knowledge transfer always help to learn a better policy"):
  with members:
    - "on the optimality of sparse model based planning for markov decision processes"
    - "sample complexity of reinforcement learning using linearly combined model ensembles"
    - "model ensemble trust region policy optimization"
    - "sample efficient reinforcement learning with stochastic ensemble value expansion"
    - "hindsight experience replay"
    - "carla an open urban driving simulator"
    - "continuous adaptation via meta learning in nonstationary and competitive environments"
    - "doubly robust off policy value evaluation for reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "on better exploring and exploiting task relationships in multitask learning joint model and feature learning"):
  with members:
    - "pareto path multitask multiple kernel learning"
    - "a dirty model for multiple sparse regression"
    - "a model of inductive bias learning"

Connected through hyperedge HE-4 (induced by source paper "a survey on multi task learning"):
  with members:
    - "learning to multitask"
    - "deep decentralized multi task multi agent reinforcement learning under partial observability"
    - "deep multi task representation learning a tensor factorisation approach"
    - "trace norm regularised deep multi task learning"
    - "modular multitask reinforcement learning with policy sketches"
    - "learning multiple visual tasks while discovering their structure"
    - "actor mimic deep multitask and transfer reinforcement learning"
    - "safe screening for multi task feature learning with multiple data matrices"

Connected through hyperedge HE-5 (induced by source paper "deep asymmetric multi task feature learning"):
  with members:
    - "deep multi task representation learning a tensor factorisation approach"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"
    - "learning task grouping and overlap in multi task learning"

Connected through hyperedge HE-6 (induced by source paper "multi agent distributed lifelong learning for collective knowledge acquisition"):
  with members:
    - "distributed multi task relationship learning"
    - "asynchronous multi task learning"
    - "multitask diffusion adaptation over networks"
    - "learning task grouping and overlap in multi task learning"
    - "distributed autonomous online learning regrets and intrinsic privacy preserving properties"

Connected through hyperedge HE-7 (induced by source paper "action affect classification and morphing using multi task representation learning"):
  with members:
    - "learning multiple visual tasks while discovering their structure"
    - "deep joint task learning for generic object extraction"
    - "domain generalization for object recognition with multi task autoencoders"
    - "multi task cnn model for attribute prediction"
    - "moddrop adaptive multi modal gesture recognition"
    - "modeling temporal dependencies in high dimensional sequences application to polyphonic music generation and transcription"
    - "learning task grouping and overlap in multi task learning"

Connected through hyperedge HE-8 (induced by source paper "cp mtml coupled projection multi task metric learning for large scale face retrieval"):
  with members:
    - "learning face representation from scratch"
    - "caffe convolutional architecture for fast feature embedding"
    - "a survey on metric learning for feature vectors and structured data"

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)