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: "can string kernels pass the test of time in native language identification"
  Abstract: We describe a machine learning approach for the 2017 shared task on Native Language Identification (NLI). The proposed approach combines several kernels using multiple kernel learning. While most of our kernels are based on character p-grams (also known as n-grams) extracted from essays or speech transcripts, we also use a kernel based on i-vectors, a low-dimensional representation of audio recordings, provided by the shared task organizers. For the learning stage, we choose Kernel Discriminant Analysis (KDA) over Kernel Ridge Regression (KRR), because the former classifier obtains better results than the latter one on the development set. In our previous work, we have used a similar machine learning approach to achieve state-of-the-art NLI results. The goal of this paper is to demonstrate that our shallow and simple approach based on string kernels (with minor improvements) can pass the test of time and reach state-of-the-art performance in the 2017 NLI shared task, despite the recent advances in natural language processing. We participated in all three tracks, in which the competitors were allowed to use only the essays (essay track), only the speech transcripts (speech track), or both (fusion track). Using only the data provided by the organizers for training our models, we have reached a macro F1 score of 86.95% in the closed essay track, a macro F1 score of 87.55% in the closed speech track, and a macro F1 score of 93.19% in the closed fusion track. With these scores, our team (UnibucKernel) ranked in the first group of teams in all three tracks, while attaining the best scores in the speech and the fusion tracks.

Connected through hyperedge HE-1 (induced by source paper "moroco the moldavian and romanian dialectal corpus"):
  with members:
    - "improving the results of string kernels in sentiment analysis and arabic dialect identification by adapting them to your test set"
    - "automatic identification of closely related indian languages resources and experiments"
    - "automated essay scoring with string kernels and word embeddings"
    - "unibuckernel reloaded first place in arabic dialect identification for the second year in a row"
    - "speech recognition challenge in the wild arabic mgb 3"
    - "language modeling with gated convolutional networks"
    - "a character level convolutional neural network for distinguishing similar languages and dialects"
    - "a convolutional encoder model for neural machine translation"

Connected through hyperedge HE-2 (induced by source paper "transductive learning with string kernels for cross domain text classification"):
  with members:
    - "improving the results of string kernels in sentiment analysis and arabic dialect identification by adapting them to your test set"
    - "automated essay scoring with string kernels and word embeddings"
    - "unibuckernel reloaded first place in arabic dialect identification for the second year in a row"
    - "return of frustratingly easy domain adaptation"
    - "frustratingly easy domain adaptation"

Connected through hyperedge HE-3 (induced by source paper "unibuckernel reloaded first place in arabic dialect identification for the second year in a row"):
  with members:
    - "convolutional neural networks and language embeddings for end to end dialect recognition"
    - "automatic language identification in texts a survey"
    - "automated essay scoring with string kernels and word embeddings"
    - "speech recognition challenge in the wild arabic mgb 3"
    - "automatic dialect detection in arabic broadcast speech"

Connected through hyperedge HE-4 (induced by source paper "improving the results of string kernels in sentiment analysis and arabic dialect identification by adapting them to your test set"):
  with members:
    - "automated essay scoring with string kernels and word embeddings"
    - "unibuckernel reloaded first place in arabic dialect identification for the second year in a row"
    - "automatic dialect detection in arabic broadcast speech"

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)