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: "syntax aware multi sense word embeddings for deep compositional models of meaning"
  Abstract: Deep compositional models of meaning acting on distributional representations of words in order to produce vectors of larger text constituents are evolving to a popular area of NLP research. We detail a compositional distributional framework based on a rich form of word embeddings that aims at facilitating the interactions between words in the context of a sentence. Embeddings and composition layers are jointly learned against a generic objective that enhances the vectors with syntactic information from the surrounding context. Furthermore, each word is associated with a number of senses, the most plausible of which is selected dynamically during the composition process. We evaluate the produced vectors qualitatively and quantitatively with positive results. At the sentence level, the effectiveness of the framework is demonstrated on the MSRPar task, for which we report results within the state-of-the-art range.

Connected through hyperedge HE-1 (induced by source paper "siamese networks for large scale author identification"):
  with members:
    - "bert rediscovers the classical nlp pipeline"
    - "generalised differential privacy for text document processing"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "node2vec scalable feature learning for networks"
    - "task guided and path augmented heterogeneous network embedding for author identification"
    - "matching networks for one shot learning"

Connected through hyperedge HE-2 (induced by source paper "understanding composition of word embeddings via tensor decomposition"):
  with members:
    - "orthogonalized als a theoretically principled tensor decomposition algorithm for practical use"
    - "advances in pre training distributed word representations"
    - "tensorflow a system for large scale machine learning"
    - "rand walk a latent variable model approach to word embeddings"
    - "adam a method for stochastic optimization"
    - "distributed representations of words and phrases and their compositionality"
    - "mathematical foundations for a compositional distributional model of meaning"
    - "most tensor problems are np hard"

Connected through hyperedge HE-3 (induced by source paper "from word to sense embeddings a survey on vector representations of meaning"):
  with members:
    - "a survey of cross lingual word embedding models"
    - "towards better ud parsing deep contextualized word embeddings ensemble and treebank concatenation"
    - "seven augmenting word embeddings with unsupervised relation vectors"
    - "incorporating glosses into neural word sense disambiguation"
    - "deep contextualized word representations"
    - "wic 10 000 example pairs for evaluating context sensitive representations"
    - "knowledge based word sense disambiguation using topic models"
    - "a robust self learning method for fully unsupervised cross lingual mappings of word embeddings"

Connected through hyperedge HE-4 (induced by source paper "semantically informed distance and similarity measures for paraphrase plagiarism identification"):
  with members:
    - "bridging the gap a semantic similarity measure between queries and documents"
    - "edit distance for pushdown automata"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-5 (induced by source paper "real multi sense or pseudo multi sense an approach to improve word representation"):
  with members:
    - "efficient non parametric estimation of multiple embeddings per word in vector space"
    - "do multi sense embeddings improve natural language understanding"
    - "a simple and efficient method to generate word sense representations"
    - "efficient estimation of word representations in vector space"
    - "exploiting similarities among languages for machine translation"

Connected through hyperedge HE-6 (induced by source paper "learning robust video synchronization without annotations"):
  with members:
    - "gated siamese convolutional neural network architecture for human re identification"
    - "warpnet weakly supervised matching for single view reconstruction"
    - "image to image translation with conditional adversarial networks"
    - "spatial transformer networks"
    - "show attend and tell neural image caption generation with visual attention"
    - "netvlad cnn architecture for weakly supervised place recognition"
    - "deep residual learning for image recognition"
    - "facenet a unified embedding for face recognition and clustering"

Connected through hyperedge HE-7 (induced by source paper "sentence pair scoring towards unified framework for text comprehension"):
  with members:
    - "attentive pooling networks"
    - "long short term memory networks for machine reading"
    - "enhancing freebase question answering using textual evidence"
    - "improved deep learning baselines for ubuntu corpus dialogs"
    - "evaluating prerequisite qualities for learning end to end dialog systems"
    - "skip thought vectors"
    - "the ubuntu dialogue corpus a large dataset for research in unstructured multi turn dialogue systems"
    - "transition based dependency parsing with stack long short term memory"

Connected through hyperedge HE-8 (induced by source paper "distributional inclusion hypothesis for tensor based composition"):
  with members:
    - "sentence entailment in compositional distributional semantics"
    - "graded entailment for compositional distributional semantics"
    - "a study of entanglement in a categorical framework of natural language"
    - "evaluating neural word representations in tensor based compositional settings"
    - "a convolutional neural network for modelling sentences"
    - "experimental support for a categorical compositional distributional model of meaning"
    - "from frequency to meaning vector space models of semantics"
    - "mathematical foundations for a compositional distributional model of meaning"

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)