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: "learning multiple tasks with deep relationship networks"
  Abstract: Deep neural networks trained on large-scale dataset can learn transferable features that promote learning multiple tasks for inductive transfer and labeling mitigation. As deep features eventually transition from general to specific along the network, a fundamental problem is how to exploit the relationship structure across different tasks while accounting for the feature transferability in the task-specific layers. In this work, we propose a novel Deep Relationship Network (DRN) architecture for multi-task learning by discovering correlated tasks based on multiple task-specific layers of a deep convolutional neural network. DRN models the task relationship by imposing matrix normal priors over the network parameters of all task-specific layers, including higher feature layers and classifier layer that are not transferable safely. By jointly learning the transferable features and task relationships, DRN is able to alleviate the dilemma of negative-transfer in the feature layers and under-transfer in the classifier layer. Empirical evidence shows that DRN yields state-of-the-art classification results on standard multi-domain object recognition datasets.

Connected through hyperedge HE-1 (induced by source paper "gradient surgery for multi task learning"):
  with members:
    - "distilling policy distillation"
    - "regularized hierarchical policies for compositional transfer in robotics"
    - "branched multi task networks deciding what layers to share"
    - "meta world a benchmark and evaluation for multi task and meta reinforcement learning"
    - "routing networks and the challenges of modular and compositional computation"
    - "ray interference a source of plateaus in deep reinforcement learning"
    - "attentive single tasking of multiple tasks"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"

Connected through hyperedge HE-2 (induced by source paper "unsupervised and interpretable domain adaptation to rapidly filter social web data for emergency services"):
  with members:
    - "diversity based generalization for neural unsupervised text classification under domain shift"
    - "domain adaptation with adversarial training and graph embeddings"
    - "advances in pre training distributed word representations"
    - "rapid classification of crisis related data on social networks using convolutional neural networks"
    - "twitter as a lifeline human annotated twitter corpora for nlp of crisis related messages"
    - "effective approaches to attention based neural machine translation"
    - "gated feedback recurrent neural networks"
    - "learning multiple tasks with multilinear relationship networks"

Connected through hyperedge HE-3 (induced by source paper "stochastic filter groups for multi task cnns learning specialist and generalist convolution kernels"):
  with members:
    - "evolutionary architecture search for deep multitask networks"
    - "taskonomy disentangling task transfer learning"
    - "uncertainty in multitask learning joint representations for probabilistic mr only radiotherapy planning"
    - "nnu net self adapting framework for u net based medical image segmentation"
    - "niftynet a deep learning platform for medical imaging"
    - "routing networks adaptive selection of non linear functions for multi task learning"
    - "on the compactness efficiency and representation of 3d convolutional networks brain parcellation as a pretext task"
    - "age progression regression by conditional adversarial autoencoder"

Connected through hyperedge HE-4 (induced by source paper "metric based regularization and temporal ensemble for multi task learning using heterogeneous unsupervised tasks"):
  with members:
    - "transfer adaptation learning a decade survey"
    - "boosting self supervised learning via knowledge transfer"
    - "improvements to context based self supervised learning"
    - "multi task self supervised visual learning"
    - "a survey on deep learning in medical image analysis"
    - "w net a deep model for fully unsupervised image segmentation"
    - "a survey on multi task learning"
    - "deep retinal image understanding"

Connected through hyperedge HE-5 (induced by source paper "context aware embeddings for automatic art analysis"):
  with members:
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "a style aware content loss for real time hd style transfer"
    - "how to read paintings semantic art understanding with multi modal retrieval"
    - "graph embedding techniques applications and performance a survey"
    - "multi task learning as multi objective optimization"
    - "an overview of multi task learning in deep neural networks"
    - "deep multi task representation learning a tensor factorisation approach"
    - "node2vec scalable feature learning for networks"

Connected through hyperedge HE-6 (induced by source paper "a self attentional neural architecture for code completion with multi task learning"):
  with members:
    - "transformer xl attentive language models beyond a fixed length context"
    - "code completion with neural attention and pointer networks"
    - "soft layer specific multi task summarization with entailment and question generation"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "sharp nearby fuzzy far away how neural language models use context"
    - "attention is all you need"
    - "an overview of multi task learning in deep neural networks"
    - "learning python code suggestion with a sparse pointer network"

Connected through hyperedge HE-7 (induced by source paper "joint detection of malicious domains and infected clients"):
  with members:
    - "discriminative models for multi instance problems with tree structure"
    - "yes machine learning can be more secure a case study on android malware detection"
    - "convolutional sequence to sequence learning"
    - "efficient hyperparameter optimization and infinitely many armed bandits"
    - "cross stitch networks for multi task learning"
    - "trace norm regularised deep multi task learning"
    - "learning multiple tasks with multilinear relationship networks"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-8 (induced by source paper "personalized attraction enhanced sponsored search with multi task learning"):
  with members:
    - "deep reinforcement learning for sponsored search real time bidding"
    - "a survey on multi task learning"
    - "an overview of multi task learning in deep neural networks"
    - "modeling attractiveness and multiple clicks in sponsored search results"
    - "truthful learning mechanisms for multi slot sponsored search auctions with externalities"
    - "distributed representations of words and phrases and their compositionality"
    - "budget optimization in search based advertising auctions"

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)