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: "incorporating sememes into chinese definition modeling"
  Abstract: Chinese definition modeling is a challenging task that generates a dictionary definition in Chinese for a given Chinese word. To accomplish this task, we construct the Chinese Definition Modeling Corpus (CDM), which contains triples of word, sememes and the corresponding definition. We present two novel models to improve Chinese definition modeling: the Adaptive-Attention model (AAM) and the Self- and Adaptive-Attention Model (SAAM). AAM successfully incorporates sememes for generating the definition with an adaptive attention mechanism. It has the capability to decide which sememes to focus on and when to pay attention to sememes. SAAM further replaces recurrent connections in AAM with self-attention and relies entirely on the attention mechanism, reducing the path length between word, sememes and definition. Experiments on CDM demonstrate that by incorporating sememes, our best proposed model can outperform the state-of-the-art method by +6.0 BLEU.

Connected through hyperedge HE-1 (induced by source paper "mark my word a sequence to sequence approach to definition modeling"):
  with members:
    - "distributional semantics and linguistic theory"
    - "transformer xl attentive language models beyond a fixed length context"
    - "xsense learning sense separated sparse representations and textual definitions for explainable word sense networks"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "scaling neural machine translation"
    - "deep contextualized word representations"
    - "universal sentence encoder"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-2 (induced by source paper "improving interpretability of word embeddings by generating definition and usage"):
  with members:
    - "xsense learning sense separated sparse representations and textual definitions for explainable word sense networks"
    - "deep contextualized word representations"
    - "semi supervised sequence modeling with cross view training"
    - "conditional generators of words definitions"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "attention is all you need"
    - "exploiting linguistic resources for neural machine translation using multi task learning"
    - "definition modeling learning to define word embeddings in natural language"

Connected through hyperedge HE-3 (induced by source paper "multi sense definition modeling using word sense decompositions"):
  with members:
    - "conditional generators of words definitions"
    - "a mixture model for learning multi sense word embeddings"
    - "learning to explain non standard english words and phrases"
    - "de conflated semantic representations"
    - "definition modeling learning to define word embeddings in natural language"
    - "linear algebraic structure of word senses with applications to polysemy"
    - "dynamic memory networks for visual and textual question answering"
    - "breaking sticks and ambiguities with adaptive skip gram"

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)