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: "discriminating similar languages evaluations and explorations"
  Abstract: We present an analysis of the performance of machine learning classifiers on discriminating between similar languages and language varieties. We carried out a number of experiments using the results of the two editions of the Discriminating between Similar Languages (DSL) shared task. We investigate the progress made between the two tasks, estimate an upper bound on possible performance using ensemble and oracle combination, and provide learning curves to help us understand which languages are more challenging. A number of difficult sentences are identified and investigated further with human annotation.

Connected through hyperedge HE-1 (induced by source paper "neural machine translation into language varieties"):
  with members:
    - "a neural approach to language variety translation"
    - "synthetic data for neural machine translation of spoken dialects"
    - "attention is all you need"
    - "transfer learning across low resource related languages for neural machine translation"
    - "six challenges for neural machine translation"
    - "toward multilingual neural machine translation with universal encoder and decoder"
    - "multi way multilingual neural machine translation with a shared attention mechanism"
    - "transfer learning for low resource neural machine translation"

Connected through hyperedge HE-2 (induced by source paper "automatic language identification in texts a survey"):
  with members:
    - "n gram and neural language models for discriminating similar languages"
    - "including dialects and language varieties in author profiling"
    - "speech recognition challenge in the wild arabic mgb 3"
    - "labeling of query words using conditional random field"
    - "language detection for short text messages in social media"
    - "automatic dialect detection in arabic broadcast speech"
    - "recurrent neural network for language detection on twitter code switching corpus"

Connected through hyperedge HE-3 (induced by source paper "a neural approach to language variety translation"):
  with members:
    - "attention is all you need"
    - "unsupervised neural machine translation"
    - "convolutional sequence to sequence learning"
    - "six challenges for neural machine translation"
    - "fully character level neural machine translation without explicit segmentation"
    - "transfer learning for low resource neural machine translation"
    - "google s neural machine translation system bridging the gap between human and machine translation"

Connected through hyperedge HE-4 (induced by source paper "n gram and neural language models for discriminating similar languages"):
  with members:
    - "convolutional recurrent neural networks for music classification"
    - "character aware neural language models"
    - "adam a method for stochastic optimization"
    - "neural machine translation by jointly learning to align and translate"
    - "deep visual semantic alignments for generating image descriptions"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-5 (induced by source paper "detecting hate speech in social media"):
  with members:
    - "automated hate speech detection and the problem of offensive language"
    - "native language identification using stacked generalization"
    - "modeling language change in historical corpora the case of portuguese"
    - "a dictionary based approach to racism detection in dutch social media"
    - "measuring the reliability of hate speech annotations the case of the european refugee crisis"

Connected through hyperedge HE-6 (induced by source paper "open set language identification"):
  with members:
    - "demographic dialectal variation in social media a case study of african american english"
    - "feature hashing for large scale multitask learning"

Connected through hyperedge HE-7 (induced by source paper "including dialects and language varieties in author profiling"):
  with members:
    - "automatic dialect detection in arabic broadcast speech"

Connected through hyperedge HE-8 (induced by source paper "byte based language identification with deep convolutional networks"):
  with members:
    - "semantic tagging with deep residual networks"
    - "identity mappings in deep residual networks"
    - "very deep convolutional networks for text classification"
    - "multilingual part of speech tagging with bidirectional long short term memory models and auxiliary loss"
    - "multi task cross lingual sequence tagging from scratch"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

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)