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: "clickbait detection in tweets using self attentive network"
  Abstract: Clickbait detection in tweets remains an elusive challenge. In this paper, we describe the solution for the Zingel Clickbait Detector at the Clickbait Challenge 2017, which is capable of evaluating each tweet's level of click baiting. We first reformat the regression problem as a multi-classification problem, based on the annotation scheme. To perform multi-classification, we apply a token-level, self-attentive mechanism on the hidden states of bi-directional Gated Recurrent Units (biGRU), which enables the model to generate tweets' task-specific vector representations by attending to important tokens. The self-attentive neural network can be trained end-to-end, without involving any manual feature engineering. Our detector ranked first in the final evaluation of Clickbait Challenge 2017.

Connected through hyperedge HE-1 (induced by source paper "federated hierarchical hybrid networks for clickbait detection"):
  with members:
    - "federated reinforcement learning"
    - "federated machine learning concept and applications"
    - "tabloids in the era of social media understanding the production and consumption of clickbaits in twitter"
    - "diving deep into clickbaits who use them to what extents in which topics with what effects"
    - "learning to identify ambiguous and misleading news headlines"
    - "stop clickbait detecting and preventing clickbaits in online news media"
    - "bag of tricks for efficient text classification"
    - "federated optimization distributed machine learning for on device intelligence"

Connected through hyperedge HE-2 (induced by source paper "clickbait in education positive or negative machine learning answers"):
  with members:
    - "natural language processing sentiment analysis and clinical analytics"
    - "characterizing iomt personal area networks landscape"
    - "making a case for social media corpus for detecting depression"
    - "byod personal area networks pans and iot threats to patients privacy"
    - "big data and social medical sciences state of the art and future trends"
    - "stop clickbait detecting and preventing clickbaits in online news media"

Connected through hyperedge HE-3 (induced by source paper "fake news a survey of research detection methods and opportunities"):
  with members:
    - "studying fake news via network analysis detection and mitigation"
    - "the rise of guardians fact checking url recommendation to combat fake news"
    - "linknbed multi graph representation learning with entity linkage"
    - "fakedetector effective fake news detection with deep diffusive neural network"
    - "belittling the source trustworthiness indicators to obfuscate fake news on the web"
    - "the spread of low credibility content by social bots"
    - "ti cnn convolutional neural networks for fake news detection"
    - "relation extraction a survey"

Connected through hyperedge HE-4 (induced by source paper "using neural network for identifying clickbaits in online news media"):
  with members:
    - "the clickbait challenge 2017 towards a regression model for clickbait strength"
    - "identifying clickbait a multi strategy approach using neural networks"
    - "a neural clickbait detection engine"
    - "identifying clickbait posts on social media with an ensemble of linear models"
    - "tabloids in the era of social media understanding the production and consumption of clickbaits in twitter"
    - "diving deep into clickbaits who use them to what extents in which topics with what effects"
    - "clickbait identification using neural networks"
    - "fishing for clickbaits in social images and texts with linguistically infused neural network models"

Connected through hyperedge HE-5 (induced by source paper "semi supervised confidence network aided gated attention based recurrent neural network for clickbait detection"):
  with members:
    - "adversarial labeling for learning without labels"
    - "how curiosity can be modeled for a clickbait detector"
    - "clickbait click based accelerated incremental training of convolutional neural networks"
    - "diving deep into clickbaits who use them to what extents in which topics with what effects"
    - "stop clickbait detecting and preventing clickbaits in online news media"
    - "we used neural networks to detect clickbaits you won t believe what happened next"
    - "show attend and tell neural image caption generation with visual attention"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-6 (induced by source paper "reinforced co training"):
  with members:
    - "the clickbait challenge 2017 towards a regression model for clickbait strength"
    - "deep reinforcement learning that matters"
    - "learning to identify ambiguous and misleading news headlines"
    - "learning to learn from noisy web videos"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "stop clickbait detecting and preventing clickbaits in online news media"
    - "neural architecture search with reinforcement learning"
    - "efficient character level document classification by combining convolution and recurrent layers"

Connected through hyperedge HE-7 (induced by source paper "identifying clickbait a multi strategy approach using neural networks"):
  with members:
    - "clickbait identification using neural networks"
    - "stop clickbait detecting and preventing clickbaits in online news media"
    - "we used neural networks to detect clickbaits you won t believe what happened next"
    - "very deep convolutional networks for large scale image recognition"
    - "distributed representations of sentences and documents"
    - "neural machine translation by jointly learning to align and translate"
    - "efficient estimation of word representations in vector space"
    - "adadelta an adaptive learning rate method"

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)