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 brief survey of text mining classification clustering and extraction techniques"
  Abstract: The amount of text that is generated every day is increasing dramatically. This tremendous volume of mostly unstructured text cannot be simply processed and perceived by computers. Therefore, efficient and effective techniques and algorithms are required to discover useful patterns. Text mining is the task of extracting meaningful information from text, which has gained significant attentions in recent years. In this paper, we describe several of the most fundamental text mining tasks and techniques including text pre-processing, classification and clustering. Additionally, we briefly explain text mining in biomedical and health care domains.

Connected through hyperedge HE-1 (induced by source paper "flowsense a natural language interface for visual data exploration within a dataflow system"):
  with members:
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "recent trends in deep learning based natural language processing"
    - "iris a conversational agent for complex tasks"
    - "compositional semantic parsing on semi structured tables"

Connected through hyperedge HE-2 (induced by source paper "better security bug report classification via hyperparameter optimization"):
  with members:
    - "bad smells in software analytics papers"
    - "is better data better than better data miners on the benefits of tuning smote for defect prediction"
    - "what is wrong with topic modeling and how to fix it using search based software engineering"
    - "hyperparameter optimization for effort estimation"
    - "less is more minimizing code reorganization using xtree"
    - "better data is better than better data miners benefits of tuning smote for defect prediction"
    - "why is differential evolution better than grid search for tuning defect predictors"
    - "tuning for software analytics"

Connected through hyperedge HE-3 (induced by source paper "an auxiliary classifier generative adversarial framework for relation extraction"):
  with members:
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "adversarial learning for neural dialogue generation"
    - "seqgan sequence generative adversarial nets with policy gradient"
    - "infogan interpretable representation learning by information maximizing generative adversarial nets"
    - "tensorflow a system for large scale machine learning"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "a deep learning approach for tweet classification and rescue scheduling for effective disaster management"):
  with members:
    - "coordinating disaster emergency response with heuristic reinforcement learning"
    - "very deep convolutional networks for text classification"
    - "rapid classification of crisis related data on social networks using convolutional neural networks"
    - "twitter as a lifeline human annotated twitter corpora for nlp of crisis related messages"
    - "effective approaches to attention based neural machine translation"
    - "learning natural language inference with lstm"
    - "speech recognition with deep recurrent neural networks"

Connected through hyperedge HE-5 (induced by source paper "ex twit explainable twitter mining on health data"):
  with members:
    - "yoga veganism correlation mining of twitter health data"
    - "explaining explanations an overview of interpretability of machine learning"
    - "twiinsight discovering topics and sentiments from social media datasets"
    - "rationalizing neural predictions"
    - "why should i trust you explaining the predictions of any classifier"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-6 (induced by source paper "yoga veganism correlation mining of twitter health data"):
  with members:
    - "why should i trust you explaining the predictions of any classifier"
    - "probabilistic latent semantic analysis"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-7 (induced by source paper "verification and validation of semantic annotations"):
  with members:
    - "heuristics for publishing dynamic content as structured data with schema org"
    - "complete semantics to empower touristic service providers"
    - "validating rdf with shape expressions"

Connected through hyperedge HE-8 (induced by source paper "augmented data science towards industrialization and democratization of data science"):
  with members:
    - "one button machine for automating feature engineering in relational databases"
    - "evaluation of a tree based pipeline optimization tool for automating data science"

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)