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: "xsense learning sense separated sparse representations and textual definitions for explainable word sense networks"
  Abstract: Despite the success achieved on various natural language processing tasks, word embeddings are difficult to interpret due to the dense vector representations. This paper focuses on interpreting the embeddings for various aspects, including sense separation in the vector dimensions and definition generation. Specifically, given a context together with a target word, our algorithm first projects the target word embedding to a high-dimensional sparse vector and picks the specific dimensions that can best explain the semantic meaning of the target word by the encoded contextual information, where the sense of the target word can be indirectly inferred. Finally, our algorithm applies an RNN to generate the textual definition of the target word in the human readable form, which enables direct interpretation of the corresponding word embedding. This paper also introduces a large and high-quality context-definition dataset that consists of sense definitions together with multiple example sentences per polysemous word, which is a valuable resource for definition modeling and word sense disambiguation. The conducted experiments show the superior performance in BLEU score and the human evaluation test.

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"
    - "incorporating sememes into chinese definition modeling"
    - "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:
    - "incorporating sememes into chinese definition modeling"
    - "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 "transformation of dense and sparse text representations"):
  with members:
    - "multi task deep neural networks for natural language understanding"
    - "consistent dialogue generation with self supervised feature learning"
    - "investigating capsule networks with dynamic routing for text classification"
    - "attention is all you need"
    - "sparse representation based open set recognition"
    - "sparse representation for 3d shape estimation a convex relaxation approach"
    - "spine sparse interpretable neural embeddings"
    - "multiresolution recurrent neural networks an application to dialogue response generation"

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)