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: "clustered multi task learning a convex formulation"
  Abstract: In multi-task learning several related tasks are considered simultaneously, with the hope that by an appropriate sharing of information across tasks, each task may benefit from the others. In the context of learning linear functions for supervised classification or regression, this can be achieved by including a priori information about the weight vectors associated with the tasks, and how they are expected to be related to each other. In this paper, we assume that tasks are clustered into groups, which are unknown beforehand, and that tasks within a group have similar weight vectors. We design a new spectral norm that encodes this a priori assumption, without the prior knowledge of the partition of tasks into groups, resulting in a new convex optimization formulation for multi-task learning. We show in simulations on synthetic examples and on the IEDB MHC-I binding dataset, that our approach outperforms well-known convex methods for multi-task learning, as well as related non convex methods dedicated to the same problem.

Connected through hyperedge HE-1 (induced by source paper "deep multi task augmented feature learning via hierarchical graph neural network"):
  with members:
    - "graph star net for generalized multi task learning"
    - "multi task learning over graph structures"
    - "learning to multitask"
    - "deep multi task representation learning a tensor factorisation approach"
    - "trace norm regularised deep multi task learning"
    - "multi domain dialog state tracking using recurrent neural networks"
    - "heterogeneous multi task learning for human pose estimation with deep convolutional neural network"
    - "a convex formulation for learning task relationships in multi task learning"

Connected through hyperedge HE-2 (induced by source paper "representative task self selection for flexible clustered lifelong learning"):
  with members:
    - "progressive neural networks"
    - "lifelong learning crf for supervised aspect extraction"
    - "continual learning through synaptic intelligence"
    - "incremental learning of object detectors without catastrophic forgetting"
    - "gradient episodic memory for continual learning"
    - "a survey on multi task learning"
    - "neurogenesis inspired dictionary learning online model adaption in a changing world"
    - "a deep hierarchical approach to lifelong learning in minecraft"

Connected through hyperedge HE-3 (induced by source paper "deep multimodality model for multi task multi view learning"):
  with members:
    - "multi task learning by deep collaboration and application in facial landmark detection"
    - "a survey on multi task learning"
    - "fully adaptive feature sharing in multi task networks with applications in person attribute classification"
    - "walk and learn facial attribute representation learning from egocentric video and contextual data"
    - "cross domain image retrieval with a dual attribute aware ranking network"
    - "very deep convolutional networks for large scale image recognition"
    - "convolutional neural networks for sentence classification"
    - "deep learning face attributes in the wild"

Connected through hyperedge HE-4 (induced by source paper "federated multi task hierarchical attention model for sensor analytics"):
  with members:
    - "meta multi task learning for sequence modeling"
    - "federated learning strategies for improving communication efficiency"
    - "correlated time series forecasting using deep neural networks a summary of results"
    - "federated multi task learning"
    - "recognizing detailed human context in the wild from smartphones and smartwatches"
    - "communication efficient learning of deep networks from decentralized data"
    - "federated optimization distributed optimization beyond the datacenter"
    - "effective approaches to attention based neural machine translation"

Connected through hyperedge HE-5 (induced by source paper "multitask hopfield networks"):
  with members:
    - "bayesian multitask learning with latent hierarchies"

Connected through hyperedge HE-6 (induced by source paper "multi task learning of pairwise sequence classification tasks over disparate label spaces"):
  with members:
    - "the repeval 2017 shared task multi genre natural language inference with sentence representations"
    - "a simple but tough to beat baseline for the fake news challenge stance detection task"
    - "semi supervised multitask learning for sequence labeling"
    - "deep multitask learning for semantic dependency parsing"
    - "learning to select data for transfer learning with bayesian optimization"
    - "recurrent neural network based sentence encoder with gated attention for natural language inference"
    - "emoji2vec learning emoji representations from their description"
    - "multilingual part of speech tagging with bidirectional long short term memory models and auxiliary loss"

Connected through hyperedge HE-7 (induced by source paper "exploiting feature and class relationships in video categorization with regularized deep neural networks"):
  with members:
    - "convolutional two stream network fusion for video action recognition"
    - "eventnet a large scale structured concept library for complex event detection in video"
    - "beyond short snippets deep networks for video classification"
    - "modeling spatial temporal clues in a hybrid deep learning framework for video classification"
    - "going deeper with convolutions"
    - "beyond gaussian pyramid multi skip feature stacking for action recognition"
    - "two stream convolutional networks for action recognition in videos"
    - "using web co occurrence statistics for improving image categorization"

Connected through hyperedge HE-8 (induced by source paper "learning to multitask"):
  with members:
    - "a survey on multi task learning"
    - "cross stitch networks for multi task learning"
    - "diffusion convolutional neural networks"
    - "very deep convolutional networks for large scale image recognition"
    - "a chain rule for the expected suprema of gaussian processes"
    - "spectral networks and locally connected networks on graphs"
    - "a convex formulation for learning task relationships in multi task learning"
    - "learning task grouping and overlap in multi task learning"

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)