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: "fakta an automatic end to end fact checking system"
  Abstract: We present FAKTA which is a unified framework that integrates various components of a fact checking process: document retrieval from media sources with various types of reliability, stance detection of documents with respect to given claims, evidence extraction, and linguistic analysis. FAKTA predicts the factuality of given claims and provides evidence at the document and sentence level to explain its predictions

Connected through hyperedge HE-1 (induced by source paper "gradient based adversarial training on transformer networks for detecting check worthy factual claims"):
  with members:
    - "how to fine tune bert for text classification"
    - "claimrank detecting check worthy claims in arabic and english"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "deep contextualized word representations"
    - "universal sentence encoder"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "towards automated factchecking developing an annotation schema and benchmark for consistent automated claim detection"
    - "supervised learning of universal sentence representations from natural language inference data"

Connected through hyperedge HE-2 (induced by source paper "neural network distiller a python package for dnn compression research"):
  with members:
    - "improving neural network quantization without retraining using outlier channel splitting"
    - "sinreq generalized sinusoidal regularization for low bitwidth deep quantized training"
    - "quantizing deep convolutional networks for efficient inference a whitepaper"
    - "attention based guided structured sparsity of deep neural networks"
    - "amc automl for model compression and acceleration on mobile devices"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "post training 4 bit quantization of convolution networks for rapid deployment"
    - "wrpn wide reduced precision networks"

Connected through hyperedge HE-3 (induced by source paper "taking a stance on fake news towards automatic disinformation assessment via deep bidirectional transformer language models for stance detection"):
  with members:
    - "assessing social and intersectional biases in contextualized word representations"
    - "seeing things from a different angle discovering diverse perspectives about claims"
    - "measuring bias in contextualized word representations"
    - "huggingface s transformers state of the art natural language processing"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "roberta a robustly optimized bert pretraining approach"
    - "a retrospective analysis of the fake news challenge stance detection task"
    - "women also snowboard overcoming bias in captioning models"

Connected through hyperedge HE-4 (induced by source paper "perspectroscope a window to the world of diverse perspectives"):
  with members:
    - "seeing things from a different angle discovering diverse perspectives about claims"
    - "fever a large scale dataset for fact extraction and verification"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "understanding and detecting supporting arguments of diverse types"

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)