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: "definition modeling learning to define word embeddings in natural language"
  Abstract: Distributed representations of words have been shown to capture lexical semantics, as demonstrated by their effectiveness in word similarity and analogical relation tasks. But, these tasks only evaluate lexical semantics indirectly. In this paper, we study whether it is possible to utilize distributed representations to generate dictionary definitions of words, as a more direct and transparent representation of the embeddings' semantics. We introduce definition modeling, the task of generating a definition for a given word and its embedding. We present several definition model architectures based on recurrent neural networks, and experiment with the models over multiple data sets. Our results show that a model that controls dependencies between the word being defined and the definition words performs significantly better, and that a character-level convolution layer designed to leverage morphology can complement word-level embeddings. Finally, an error analysis suggests that the errors made by a definition model may provide insight into the shortcomings of word embeddings.

Connected through hyperedge HE-1 (induced by source paper "probing for semantic classes diagnosing the meaning content of word embeddings"):
  with members:
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "from word to sense embeddings a survey on vector representations of meaning"
    - "semantic structure and interpretability of word embeddings"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "conditional generators of words definitions"
    - "factors influencing the surprising instability of word embeddings"
    - "what you can cram into a single vector probing sentence embeddings for linguistic properties"

Connected through hyperedge HE-2 (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"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "improving interpretability of word embeddings by generating definition and usage"):
  with members:
    - "incorporating sememes into chinese definition modeling"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "multi sense definition modeling using word sense decompositions"):
  with members:
    - "incorporating sememes into chinese definition modeling"
    - "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"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "learning to decipher hate symbols"):
  with members:
    - "generating sentences by editing prototypes"
    - "recognizing explicit and implicit hate speech using a weakly supervised two path bootstrapping approach"
    - "learning to explain non standard english words and phrases"
    - "learning discourse level diversity for neural dialog models using conditional variational autoencoders"
    - "ex machina personal attacks seen at scale"
    - "variational neural machine translation"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "learning to understand phrases by embedding the dictionary"

Connected through hyperedge HE-6 (induced by source paper "incorporating sememes into chinese definition modeling"):
  with members:
    - "conditional generators of words definitions"
    - "constituency parsing with a self attentive encoder"
    - "attention is all you need"
    - "deep semantic role labeling with self attention"
    - "get to the point summarization with pointer generator networks"
    - "a structured self attentive sentence embedding"
    - "long short term memory networks for machine reading"
    - "a decomposable attention model for natural language inference"

Connected through hyperedge HE-7 (induced by source paper "definition frames using definitions for hybrid concept representations"):
  with members:
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "enriching word vectors with subword information"
    - "counter fitting word vectors to linguistic constraints"
    - "problems with evaluation of word embeddings using word similarity tasks"
    - "simverb 3500 a large scale evaluation set of verb similarity"

Connected through hyperedge HE-8 (induced by source paper "inducing and embedding senses with scaled gumbel softmax"):
  with members:
    - "from word to sense embeddings a survey on vector representations of meaning"
    - "deep contextualized word representations"
    - "wic 10 000 example pairs for evaluating context sensitive representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "evaluation of sentence embeddings in downstream and linguistic probing tasks"
    - "probabilistic fasttext for multi sense word embeddings"
    - "a mixture model for learning multi sense word embeddings"
    - "muse modularizing unsupervised sense embeddings"

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)