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: "scattertext a browser based tool for visualizing how corpora differ"
  Abstract: Scattertext is an open source tool for visualizing linguistic variation between document categories in a language-independent way. The tool presents a scatterplot, where each axis corresponds to the rank-frequency a term occurs in a category of documents. Through a tie-breaking strategy, the tool is able to display thousands of visible term-representing points and find space to legibly label hundreds of them. Scattertext also lends itself to a query-based visualization of how the use of terms with similar embeddings differs between document categories, as well as a visualization for comparing the importance scores of bag-of-words features to univariate metrics.

Connected through hyperedge HE-1 (induced by source paper "diversity density and homogeneity quantitative characteristic metrics for text collections"):
  with members:
    - "casa nlu context aware self attentive natural language understanding for task oriented chatbots"
    - "simple fast accurate intent classification and slot labeling for goal oriented dialogue systems"
    - "joint slot filling and intent detection via capsule neural networks"
    - "deep contextualized word representations"
    - "fine tuned language models for text classification"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "snips voice platform an embedded spoken language understanding system for private by design voice interfaces"
    - "universal language model fine tuning for text classification"

Connected through hyperedge HE-2 (induced by source paper "goal embedded dual hierarchical model for task oriented dialogue generation"):
  with members:
    - "a repository of conversational datasets"
    - "simple fast accurate intent classification and slot labeling for goal oriented dialogue systems"
    - "simple fast accurate intent classification and slot labeling"
    - "better conversations by modeling filtering and optimizing for coherence and diversity"
    - "data augmentation for spoken language understanding via joint variational generation"
    - "a hierarchical latent structure for variational conversation modeling"
    - "global locally self attentive dialogue state tracker"
    - "on the state of the art of evaluation in neural language models"

Connected through hyperedge HE-3 (induced by source paper "diamonds in the rough generating fluent sentences from early stage drafts for academic writing assistance"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "corpora generation for grammatical error correction"
    - "bertscore evaluating text generation with bert"
    - "improving grammatical error correction via pre training a copy augmented architecture with unlabeled data"
    - "text infilling"
    - "style transfer through back translation"
    - "relevance of unsupervised metrics in task oriented dialogue for evaluating natural language generation"
    - "content preserving text generation with attribute controls"

Connected through hyperedge HE-4 (induced by source paper "research frontiers in transfer learning a systematic and bibliometric review"):
  with members:
    - "skin lesion segmentation using u net and good training strategies"
    - "taskonomy disentangling task transfer learning"
    - "exploring the limits of weakly supervised pretraining"
    - "fine tuned language models for text classification"
    - "deep hashing network for unsupervised domain adaptation"
    - "adversarial examples for evaluating reading comprehension systems"
    - "borrowing treasures from the wealthy deep transfer learning through selective joint fine tuning"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-5 (induced by source paper "trouble with the curve predicting future mlb players using scouting reports"):
  with members:
    - "understanding convolutional neural networks for text classification"
    - "allennlp a deep semantic natural language processing platform"

Connected through hyperedge HE-6 (induced by source paper "ti cnn convolutional neural networks for fake news detection"):
  with members:
    - "liar liar pants on fire a new benchmark dataset for fake news detection"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "abc cnn an attention based convolutional neural network for visual question answering"
    - "computational fact checking from knowledge networks"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"
    - "convolutional neural networks for sentence classification"

Connected through hyperedge HE-7 (induced by source paper "n gram new groningen author profiling model"):
  with members:
    - "a low dimensionality representation for language variety identification"
    - "bag of tricks for efficient text classification"
    - "gender identity and lexical variation in social media"

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)