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: "building a word segmenter for sanskrit overnight"
  Abstract: There is an abundance of digitised texts available in Sanskrit. However, the word segmentation task in such texts are challenging due to the issue of 'Sandhi'. In Sandhi, words in a sentence often fuse together to form a single chunk of text, where the word delimiter vanishes and sounds at the word boundaries undergo transformations, which is also reflected in the written text. Here, we propose an approach that uses a deep sequence to sequence (seq2seq) model that takes only the sandhied string as the input and predicts the unsandhied string. The state of the art models are linguistically involved and have external dependencies for the lexical and morphological analysis of the input. Our model can be trained "overnight" and be used for production. In spite of the knowledge lean approach, our system preforms better than the current state of the art by gaining a percentage increase of 16.79 % than the current state of the art.

Connected through hyperedge HE-1 (induced by source paper "upcycle your ocr reusing ocrs for post ocr text correction in romanised sanskrit"):
  with members:
    - "synthetic and natural noise both break neural machine translation"
    - "incorporating copying mechanism in sequence to sequence learning"
    - "neural architectures for named entity recognition"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "effective approaches to attention based neural machine translation"
    - "neural machine translation of rare words with subword units"
    - "neural machine translation by jointly learning to align and translate"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-2 (induced by source paper "free as in free word order an energy based model for word segmentation and morphological tagging in sanskrit"):
  with members:
    - "achieving human parity on automatic chinese to english news translation"
    - "classical structured prediction losses for sequence to sequence learning"
    - "neural word segmentation with rich pretraining"
    - "enriching word vectors with subword information"
    - "globally normalized transition based neural networks"
    - "structured prediction energy networks"
    - "neural machine translation by jointly learning to align and translate"
    - "natural language processing almost from scratch"

Connected through hyperedge HE-3 (induced by source paper "sanskrit sandhi splitting using pmb seq2 seq 2"):
  with members:
    - "effective approaches to attention based neural machine translation"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "how to construct deep recurrent neural networks"
    - "speech recognition with deep recurrent neural networks"

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)