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: "automated essay scoring with string kernels and word embeddings"
  Abstract: In this work, we present an approach based on combining string kernels and word embeddings for automatic essay scoring. String kernels capture the similarity among strings based on counting common character n-grams, which are a low-level yet powerful type of feature, demonstrating state-of-the-art results in various text classification tasks such as Arabic dialect identification or native language identification. To our best knowledge, we are the first to apply string kernels to automatically score essays. We are also the first to combine them with a high-level semantic feature representation, namely the bag-of-super-word-embeddings. We report the best performance on the Automated Student Assessment Prize data set, in both in-domain and cross-domain settings, surpassing recent state-of-the-art deep learning approaches.

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"
    - "unibuckernel reloaded first place in arabic dialect identification for the second year in a row"
    - "can string kernels pass the test of time in native language identification"
    - "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 "vector of locally aggregated word embeddings vlawe a novel document level representation"):
  with members:
    - "baseline needs more love on simple word embedding based models and associated pooling mechanisms"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "shotgunwsd an unsupervised algorithm for global word sense disambiguation inspired by dna sequencing"
    - "from image to text classification a novel approach based on clustering word embeddings"
    - "dynamic compositional neural networks over tree structure"
    - "text classification improved by integrating bidirectional lstm with two dimensional max pooling"
    - "learning distributed representations of sentences from unlabelled data"
    - "skip thought vectors"

Connected through hyperedge HE-3 (induced by source paper "automated essay scoring based on two stage learning"):
  with members:
    - "neural automated essay scoring and coherence modeling for adversarially crafted input"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "xgboost a scalable tree boosting system"
    - "automatic text scoring using neural networks"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-4 (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"
    - "unibuckernel reloaded first place in arabic dialect identification for the second year in a row"
    - "can string kernels pass the test of time in native language identification"
    - "return of frustratingly easy domain adaptation"
    - "frustratingly easy domain adaptation"

Connected through hyperedge HE-5 (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"
    - "can string kernels pass the test of time in native language identification"
    - "speech recognition challenge in the wild arabic mgb 3"
    - "automatic dialect detection in arabic broadcast speech"

Connected through hyperedge HE-6 (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:
    - "unibuckernel reloaded first place in arabic dialect identification for the second year in a row"
    - "can string kernels pass the test of time in native language identification"
    - "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)