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: "a hierarchical multi task approach for learning embeddings from semantic tasks"
  Abstract: Much effort has been devoted to evaluate whether multi-task learning can be leveraged to learn rich representations that can be used in various Natural Language Processing (NLP) down-stream applications. However, there is still a lack of understanding of the settings in which multi-task learning has a significant effect. In this work, we introduce a hierarchical model trained in a multi-task learning setup on a set of carefully selected semantic tasks. The model is trained in a hierarchical fashion to introduce an inductive bias by supervising a set of low level tasks at the bottom layers of the model and more complex tasks at the top layers of the model. This model achieves state-of-the-art results on a number of tasks, namely Named Entity Recognition, Entity Mention Detection and Relation Extraction without hand-engineered features or external NLP tools like syntactic parsers. The hierarchical training supervision induces a set of shared semantic representations at lower layers of the model. We show that as we move from the bottom to the top layers of the model, the hidden states of the layers tend to represent more complex semantic information.

Connected through hyperedge HE-1 (induced by source paper "sbert wk a sentence embedding method by dissecting bert based word models"):
  with members:
    - "are sixteen heads really better than one"
    - "language models as knowledge bases"
    - "pitfalls in the evaluation of sentence embeddings"
    - "evaluating word embedding models methods and experimental results"
    - "sentence bert sentence embeddings using siamese bert networks"
    - "albert a lite bert for self supervised learning of language representations"
    - "how contextual are contextualized word representations comparing the geometry of bert elmo and gpt 2 embeddings"
    - "revealing the dark secrets of bert"

Connected through hyperedge HE-2 (induced by source paper "product knowledge graph embedding for e commerce"):
  with members:
    - "analogies explained towards understanding word embeddings"
    - "a multi task learning approach for improving product title compression with user search log data"
    - "ripplenet propagating user preferences on the knowledge graph for recommender systems"
    - "multi task learning as multi objective optimization"
    - "learning general purpose distributed sentence representations via large scale multi task learning"
    - "attention is all you need"
    - "gradnorm gradient normalization for adaptive loss balancing in deep multitask networks"
    - "poincar e embeddings for learning hierarchical representations"

Connected through hyperedge HE-3 (induced by source paper "auxiliary learning for deep multi task learning"):
  with members:
    - "on the optimization of deep networks implicit acceleration by overparameterization"
    - "deep attention based classification network for robust depth prediction"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "ocnet object context network for scene parsing"
    - "deep ordinal regression network for monocular depth estimation"
    - "discrimination aware channel pruning for deep neural networks"
    - "fast neural architecture search of compact semantic segmentation models via auxiliary cells"

Connected through hyperedge HE-4 (induced by source paper "bam born again multi task networks for natural language understanding"):
  with members:
    - "multilingual neural machine translation with knowledge distillation"
    - "multi task deep neural networks for natural language understanding"
    - "unifying question answering text classification and regression via span extraction"
    - "improving multi task deep neural networks via knowledge distillation for natural language understanding"
    - "unifying question answering and text classification via span extraction"
    - "sentence encoders on stilts supplementary training on intermediate labeled data tasks"
    - "the natural language decathlon multitask learning as question answering"
    - "can you tell me how to get past sesame street sentence level pretraining beyond language modeling"

Connected through hyperedge HE-5 (induced by source paper "multinet multi stream feature aggregation and geometric loss strategy for multi task learning"):
  with members:
    - "multi stream cnn based video semantic segmentation for automated driving"
    - "neurall towards a unified model for visual perception in automated driving"
    - "auxnet auxiliary tasks enhanced semantic segmentation for automated driving"
    - "end to end multi task learning with attention"
    - "multi task learning as multi objective optimization"
    - "digrad multi task reinforcement learning with shared actions"
    - "universal representations the missing link between faces text planktons and cat breeds"
    - "flow guided feature aggregation for video object detection"

Connected through hyperedge HE-6 (induced by source paper "bert and pals projected attention layers for efficient adaptation in multi task learning"):
  with members:
    - "multi task deep neural networks for natural language understanding"
    - "transformer xl attentive language models beyond a fixed length context"
    - "sentence encoders on stilts supplementary training on intermediate labeled data tasks"
    - "the natural language decathlon multitask learning as question answering"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "swag a large scale adversarial dataset for grounded commonsense inference"
    - "learning general purpose distributed sentence representations via large scale multi task learning"

Connected through hyperedge HE-7 (induced by source paper "better faster stronger sequence tagging constituent parsers"):
  with members:
    - "multilingual constituency parsing with self attention and pre training"
    - "deep contextualized word representations"
    - "constituent parsing as sequence labeling"
    - "straight to the tree constituency parsing with neural syntactic distance"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "ncrf an open source neural sequence labeling toolkit"
    - "constituency parsing with a self attentive encoder"
    - "policy gradient as a proxy for dynamic oracles in constituency parsing"

Connected through hyperedge HE-8 (induced by source paper "learning from dialogue after deployment feed yourself chatbot"):
  with members:
    - "beyond turing intelligent agents centered on the user"
    - "bootstrapping conversational agents with weak supervision"
    - "training millions of personalized dialogue agents"
    - "dialogue learning with human teaching and feedback in end to end trainable task oriented dialogue systems"
    - "learning to ask good questions ranking clarification questions using neural expected value of perfect information"
    - "interactive language acquisition with one shot visual concept learning through a conversational game"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "listen interact and talk learning to speak via interaction"

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)