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 learning of multiple tasks and their structure"
  Abstract: Reducing the amount of human supervision is a key problem in machine learning and a natural approach is that of exploiting the relations (structure) among different tasks. This is the idea at the core of multi-task learning. In this context a fundamental question is how to incorporate the tasks structure in the learning problem.We tackle this question by studying a general computational framework that allows to encode a-priori knowledge of the tasks structure in the form of a convex penalty; in this setting a variety of previously proposed methods can be recovered as special cases, including linear and non-linear approaches. Within this framework, we show that tasks and their structure can be efficiently learned considering a convex optimization problem that can be approached by means of block coordinate methods such as alternating minimization and for which we prove convergence to the global minimum.

Connected through hyperedge HE-1 (induced by source paper "weakly supervised open set domain adaptation by dual domain collaboration"):
  with members:
    - "unsupervised person re identification by deep asymmetric metric embedding"
    - "open set domain adaptation by backpropagation"
    - "partial adversarial domain adaptation"
    - "deep asymmetric multi task feature learning"
    - "image image domain adaptation with preserved self similarity and domain dissimilarity for person re identification"
    - "adversarial discriminative domain adaptation"
    - "unsupervised person re identification clustering and fine tuning"
    - "a survey on multi task learning"

Connected through hyperedge HE-2 (induced by source paper "a principled approach for learning task similarity in multitask learning"):
  with members:
    - "robust learning from untrusted sources"
    - "multinomial adversarial networks for multi domain text classification"
    - "on the inductive bias of word character level multi task learning for speech recognition"
    - "distance measure machines"
    - "wasserstein distance measure machines"
    - "minimax statistical learning with wasserstein distances"
    - "adversarial multi task learning for text classification"
    - "improved training of wasserstein gans"

Connected through hyperedge HE-3 (induced by source paper "multi task kernel null space for one class classification"):
  with members:
    - "one class kernel spectral regression"
    - "one class kernel spectral regression for outlier detection"
    - "deepkspd learning kernel matrix based spd representation for fine grained image recognition"
    - "fast multi output relevance vector regression"
    - "a survey on multi task 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 "consistent multitask learning with nonlinear output relations"):
  with members:
    - "a consistent regularization approach for structured prediction"
    - "a convex formulation for learning task relationships in multi task learning"
    - "a convex feature learning formulation for latent task structure discovery"
    - "clustered multi task learning a convex formulation"
    - "learning to learn"

Connected through hyperedge HE-6 (induced by source paper "coordinated online learning with applications to learning user preferences"):
  with members:
    - "distributed learning communication complexity and privacy"
    - "differentially private online learning"
    - "optimal distributed online prediction using mini batches"

Connected through hyperedge HE-7 (induced by source paper "a vector contraction inequality for rademacher complexities"):
  with members:
    - "generalization error bounds for learning to rank does the length of document lists matter"
    - "multi class svms from tighter data dependent generalization bounds to novel algorithms"
    - "regularization techniques for learning with matrices"
    - "a model of inductive bias learning"

Connected through hyperedge HE-8 (induced by source paper "learning multiple tasks with multilinear relationship networks"):
  with members:
    - "deep multi task representation learning a tensor factorisation approach"
    - "cross stitch networks for multi task learning"
    - "learning transferable features with deep adaptation networks"
    - "how transferable are features in deep neural networks"
    - "very deep convolutional networks for large scale image recognition"
    - "lsda large scale detection through adaptation"
    - "caffe convolutional architecture for fast feature embedding"
    - "decaf a deep convolutional activation feature for generic visual recognition"

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)