human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "hate speech detection a solved problem the challenging case of long tail on twitter"):
  Abstract: In recent years, the increasing propagation of hate speech on social media and the urgent need for effective counter-measures have drawn significant investment from governments, companies, and empirical research. Despite a large number of emerging, scientific studies to address the problem, the performance of existing automated methods at identifying specific types of hate speech - as opposed to identifying non-hate -is still very unsatisfactory, and the reasons behind are poorly understood. This work undertakes the first in-depth analysis towards this problem and shows that, the very challenging nature of identifying hate speech on the social media is largely due to the extremely unbalanced presence of real hateful content in the typical datasets, and the lack of unique, discriminative features in such content, both causing them to reside in the 'long tail' of a dataset that is difficult to discover. To address this issue, we propose novel Deep Neural Network structures serving as effective feature extractors, and explore the usage of background information in the form of different word embeddings pre-trained from unlabelled corpora. We empirically evaluate our methods on the largest collection of hate speech datasets based on Twitter, and show that our methods can significantly outperform state of the art, as they are able to obtain a maximum improvement of between 4 and 16 percentage points (macro-average F1) depending on datasets.

Includes members:
  Member "like sheep among wolves characterizing hateful users on twitter":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "discovering differential features adversarial learning for information credibility evaluation")
  Member "automated hate speech detection and the problem of offensive language":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "ten social dimensions of conversations and relationships")
  Member "deep learning for hate speech detection in tweets":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "ties temporal interaction embeddings for enhancing social media integrity at facebook")
  Member "one step and two step classification for abusive language detection on twitter":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "nlpr srpol at semeval 2019 task 6 and task 5 linguistically enhanced deep learning offensive sentence classifier")
  Member "data sets word embeddings learned from tweets and general data":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "using similarity measures to select pretraining data for ner")
  Member "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "relationship embedded representation learning for grounding referring expressions")
  Member "adam a method for stochastic optimization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "empirical evaluation of gated recurrent neural networks on sequence modeling":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")

Question: Please tell me which class the source paper 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)