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: "adversarial multi task learning for text classification"
  Abstract: Neural network models have shown their promising opportunities for multi-task learning, which focus on learning the shared layers to extract the common and task-invariant features. However, in most existing approaches, the extracted shared features are prone to be contaminated by task-specific features or the noise brought by other tasks. In this paper, we propose an adversarial multi-task learning framework, alleviating the shared and private latent feature spaces from interfering with each other. We conduct extensive experiments on 16 different text classification tasks, which demonstrates the benefits of our approach. Besides, we show that the shared knowledge learned by our proposed model can be regarded as off-the-shelf knowledge and easily transferred to new tasks. The datasets of all 16 tasks are publicly available at \url{this http URL}

Connected through hyperedge HE-1 (induced by source paper "multi source domain adaptation for text classification via distancenet bandits"):
  with members:
    - "beyond sharing weights for deep domain adaptation"
    - "autosem automatic task selection and mixing in multi task learning"
    - "a survey of domain adaptation for neural machine translation"
    - "deep visual domain adaptation a survey"
    - "multi source domain adaptation with mixture of experts"
    - "unsupervised domain adaptation based on source guided discrepancy"
    - "deep contextualized word representations"
    - "multi source cross lingual model transfer learning what to share"

Connected through hyperedge HE-2 (induced by source paper "multi source domain adaptation in the deep learning era a systematic survey"):
  with members:
    - "multi source domain adaptation for text classification via distancenet bandits"
    - "multi source domain adaptation for visual sentiment classification"
    - "contrastive adaptation network for unsupervised domain adaptation"
    - "domain randomization and pyramid consistency simulation to real generalization without accessing target domain data"
    - "multi source domain adaptation for semantic segmentation"
    - "multi source distilling domain adaptation"
    - "moment matching for multi source domain adaptation"
    - "learning to adapt structured output space for semantic segmentation"

Connected through hyperedge HE-3 (induced by source paper "depth adaptive graph recurrent network for text classification"):
  with members:
    - "graph based neural sentence ordering"
    - "depth adaptive transformer"
    - "r transformer recurrent neural network enhanced transformer"
    - "learning to remember more with less memorization"
    - "densely connected graph convolutional networks for graph to sequence learning"
    - "cm net a novel collaborative memory network for spoken language understanding"
    - "universal transformers"
    - "xlnet generalized autoregressive pretraining for language understanding"

Connected through hyperedge HE-4 (induced by source paper "learning to multi task learn for better neural machine translation"):
  with members:
    - "autosem automatic task selection and mixing in multi task learning"
    - "neural machine translation for bilingually scarce scenarios a deep multi task learning approach"
    - "soft layer specific multi task summarization with entailment and question generation"
    - "learning to reweight examples for robust deep learning"
    - "scheduled multi task learning from syntax to translation"
    - "learning to multitask"
    - "opennmt open source toolkit for neural machine translation"
    - "mentornet learning data driven curriculum for very deep neural networks on corrupted labels"

Connected through hyperedge HE-5 (induced by source paper "a simple general approach to balance task difficulty in multi task learning"):
  with members:
    - "pareto multi task learning"
    - "multi task learning as multi objective optimization"
    - "deep hashing network for unsupervised domain adaptation"
    - "gradnorm gradient normalization for adaptive loss balancing in deep multitask networks"
    - "deep multi task representation learning a tensor factorisation approach"
    - "trace norm regularised deep multi task learning"
    - "learning multiple tasks with multilinear relationship networks"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "multi task learning with auxiliary speaker identification for conversational emotion recognition"):
  with members:
    - "multi task networks with universe group and task feature learning"
    - "knowledge enriched transformer for emotion detection in textual conversations"
    - "higru hierarchical gated recurrent units for utterance level emotion recognition"
    - "multi task learning with language modeling for question generation"
    - "dialoguegcn a graph convolutional neural network for emotion recognition in conversation"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "end to end multimodal emotion recognition using deep neural networks"

Connected through hyperedge HE-7 (induced by source paper "a label proportions estimation technique for adversarial domain adaptation in text classification"):
  with members:
    - "regularized learning for domain adaptation under label shifts"
    - "multinomial adversarial networks for multi domain text classification"
    - "detecting and correcting for label shift with black box predictors"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-8 (induced by source paper "attentive single tasking of multiple tasks"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "adversarial collaboration joint unsupervised learning of depth camera motion optical flow and motion segmentation"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "a modulation module for multi task learning with applications in image retrieval"
    - "df net unsupervised joint learning of depth and flow using cross task consistency"
    - "taskonomy disentangling task transfer learning"
    - "unified perceptual parsing for scene understanding"
    - "efficient video object segmentation via network modulation"

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.CL(Computation and Language)