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: "joint embedding of words and labels for text classification"
  Abstract: Word embeddings are effective intermediate representations for capturing semantic regularities between words, when learning the representations of text sequences. We propose to view text classification as a label-word joint embedding problem: each label is embedded in the same space with the word vectors. We introduce an attention framework that measures the compatibility of embeddings between text sequences and labels. The attention is learned on a training set of labeled samples to ensure that, given a text sequence, the relevant words are weighted higher than the irrelevant ones. Our method maintains the interpretability of word embeddings, and enjoys a built-in ability to leverage alternative sources of information, in addition to input text sequences. Extensive results on the several large text datasets show that the proposed framework outperforms the state-of-the-art methods by a large margin, in terms of both accuracy and speed.

Connected through hyperedge HE-1 (induced by source paper "description based text classification with reinforcement learning"):
  with members:
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "utilizing bert for aspect based sentiment analysis via constructing auxiliary sentence"
    - "docbert bert for document classification"
    - "question answering is a format when is it useful"
    - "entity relation extraction as multi turn question answering"
    - "integrating semantic knowledge to tackle zero shot text classification"
    - "coreference resolution as query based span prediction"
    - "putting the horse before the cart a generator evaluator framework for question generation from text"

Connected through hyperedge HE-2 (induced by source paper "meta embeddings based on self attention"):
  with members:
    - "time aware large kernel convolutions"
    - "pay less attention with lightweight and dynamic convolutions"
    - "transformer xl attentive language models beyond a fixed length context"
    - "improving neural language modeling via adversarial training"
    - "generating long sequences with sparse transformers"
    - "adaptive attention span in transformers"
    - "weighted transformer network for machine translation"
    - "frustratingly easy meta embedding computing meta embeddings by averaging source word embeddings"

Connected through hyperedge HE-3 (induced by source paper "discriminative topic mining via category name guided text embedding"):
  with members:
    - "guiding corpus based set expansion by auxiliary sets generation and co expansion"
    - "spherical text embedding"
    - "higitclass keyword driven hierarchical classification of github repositories"
    - "improving topic models with latent feature word representations"
    - "learning continuous hierarchies in the lorentz model of hyperbolic geometry"
    - "distilled wasserstein learning for word embedding and topic modeling"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "weakly supervised hierarchical text classification"

Connected through hyperedge HE-4 (induced by source paper "if i hear you correctly building and evaluating interview chatbots with active listening skills"):
  with members:
    - "tell me about yourself using an ai powered chatbot to conduct conversational surveys"
    - "universal sentence encoder"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "touch your heart a tone aware chatbot for customer care on social media"
    - "emotional chatting machine emotional conversation generation with internal and external memory"
    - "learning to ask neural question generation for reading comprehension"
    - "learning conversational systems that interleave task and non task content"
    - "axiomatic attribution for deep networks"

Connected through hyperedge HE-5 (induced by source paper "tensor graph convolutional networks for text classification"):
  with members:
    - "mr gnn multi resolution and dual graph neural network for predicting structured entity interactions"
    - "multi channel graph neural network for entity alignment"
    - "multi hop reading comprehension across multiple documents by reasoning over heterogeneous graphs"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "relational inductive biases deep learning and graph networks"
    - "baseline needs more love on simple word embedding based models and associated pooling mechanisms"
    - "incorporating syntactic and semantic information in word embeddings using graph convolutional networks"
    - "graph convolutional encoders for syntax aware neural machine translation"

Connected through hyperedge HE-6 (induced by source paper "label guided learning for text classification"):
  with members:
    - "bi directional block self attention for fast and memory efficient sequence modeling"
    - "relational inductive biases deep learning and graph networks"
    - "gile a generalized input label embedding for text classification"
    - "a comprehensive survey of graph embedding problems techniques and applications"
    - "semi supervised classification with graph convolutional networks"
    - "recurrent neural network for text classification with multi task learning"
    - "bag of tricks for efficient text classification"
    - "deep convolutional networks on graph structured data"

Connected through hyperedge HE-7 (induced by source paper "rest a thread embedding approach for identifying and classifying user specified information in security forums"):
  with members:
    - "ripex extracting malicious ip addresses from security forums using cross forum learning"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "baseline needs more love on simple word embedding based models and associated pooling mechanisms"
    - "early warnings of cyber threats in online discussions"
    - "relevance based word embedding"
    - "bag of tricks for efficient text classification"
    - "pte predictive text embedding through large scale heterogeneous text networks"
    - "distributed representations of sentences and documents"

Connected through hyperedge HE-8 (induced by source paper "short text classification via term graph"):
  with members:
    - "end to end learning for short text expansion"
    - "bag of tricks for efficient text classification"
    - "convolutional neural networks for sentence classification"

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)