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: "diffusion maps for textual network embedding"
  Abstract: Textual network embedding leverages rich text information associated with the network to learn low-dimensional vectorial representations of vertices. Rather than using typical natural language processing (NLP) approaches, recent research exploits the relationship of texts on the same edge to graphically embed text. However, these models neglect to measure the complete level of connectivity between any two texts in the graph. We present diffusion maps for textual network embedding (DMTE), integrating global structural information of the graph to capture the semantic relatedness between texts, with a diffusion-convolution operation applied on the text inputs. In addition, a new objective function is designed to efficiently preserve the high-order proximity using the graph diffusion. Experimental results show that the proposed approach outperforms state-of-the-art methods on the vertex-classification and link-prediction tasks.

Connected through hyperedge HE-1 (induced by source paper "dialogue history integration into end to end signal to concept spoken language understanding systems"):
  with members:
    - "hyst a hybrid approach for flexible and accurate dialogue state tracking"
    - "context aware neural based dialog act classification on automatically generated transcriptions"
    - "sumbt slot utterance matching for universal and scalable belief tracking"
    - "incremental transformer with deliberation decoder for document grounded conversations"
    - "do neural dialog systems use the conversation history effectively an empirical study"
    - "curriculum based transfer learning for an effective end to end spoken language understanding and domain portability"
    - "a hierarchical decoding model for spoken language understanding from unaligned data"
    - "probing for semantic classes diagnosing the meaning content of word embeddings"

Connected through hyperedge HE-2 (induced by source paper "graph neighborhood attentive pooling"):
  with members:
    - "is a single embedding enough learning node representations that capture multiple social contexts"
    - "mixhop higher order graph convolutional architectures via sparsified neighborhood mixing"
    - "simplifying graph convolutional networks"
    - "graph convolutional neural networks for web scale recommender systems"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attentive pooling networks"
    - "node2vec scalable feature learning for networks"

Connected through hyperedge HE-3 (induced by source paper "improving textual network embedding with global attention via optimal transport"):
  with members:
    - "improving sequence to sequence learning via optimal transport"
    - "gromov wasserstein alignment of word embedding spaces"
    - "adversarial text generation via feature mover s distance"
    - "soft layer specific multi task summarization with entailment and question generation"
    - "improved semantic aware network embedding with fine grained word alignment"
    - "joint embedding of words and labels for text classification"
    - "stochastic answer networks for natural language inference"
    - "network representation learning a survey"

Connected through hyperedge HE-4 (induced by source paper "improving textual network learning with variational homophilic embeddings"):
  with members:
    - "topic guided variational autoencoders for text generation"
    - "pytorch biggraph a large scale graph embedding system"
    - "network structure from rich but noisy data"
    - "understanding convolutional neural networks for text classification"
    - "constrained graph variational autoencoders for molecule design"
    - "graphvae towards generation of small graphs using variational autoencoders"
    - "interpretable vaes for nonlinear group factor analysis"
    - "nash toward end to end neural architecture for generative semantic hashing"

Connected through hyperedge HE-5 (induced by source paper "dynamic embedding on textual networks via a gaussian process"):
  with members:
    - "graph neural networks for social recommendation"
    - "graph convolutional neural networks for web scale recommender systems"
    - "nscaching simple and efficient negative sampling for knowledge graph embedding"
    - "dyngem deep embedding method for dynamic graphs"
    - "improved semantic aware network embedding with fine grained word alignment"
    - "spine structural identity preserved inductive network embedding"
    - "representation learning over dynamic graphs"
    - "predicting multicellular function through multi layer tissue networks"

Connected through hyperedge HE-6 (induced by source paper "gaussian process based dynamic embedding for textual networks"):
  with members:
    - "graph neural networks for social recommendation"
    - "graph convolutional neural networks for web scale recommender systems"
    - "nscaching simple and efficient negative sampling for knowledge graph embedding"
    - "bayesian semi supervised learning with graph gaussian processes"
    - "universal sentence encoder"
    - "know evolve deep temporal reasoning for dynamic knowledge graphs"
    - "watch your step learning node embeddings via graph attention"
    - "attributed network embedding for learning in a dynamic environment"

Connected through hyperedge HE-7 (induced by source paper "improved semantic aware network embedding with fine grained word alignment"):
  with members:
    - "multi view sentence representation learning"
    - "nash toward end to end neural architecture for generative semantic hashing"
    - "a simple neural network module for relational reasoning"
    - "deconvolutional latent variable model for text sequence matching"
    - "deconvolutional paragraph representation learning"
    - "bilateral multi perspective matching for natural language sentences"
    - "a decomposable attention model for natural language inference"
    - "node2vec scalable feature learning for networks"

Connected through hyperedge HE-8 (induced by source paper "adaptive convolutional filter generation for natural language understanding"):
  with members:
    - "multi view sentence representation learning"
    - "nash toward end to end neural architecture for generative semantic hashing"
    - "joint embedding of words and labels for text classification"
    - "topic compositional neural language model"
    - "deconvolutional latent variable model for text sequence matching"
    - "dynamic compositional neural networks over tree structure"
    - "learning loss functions for semi supervised learning via discriminative adversarial networks"
    - "convolutional sequence to sequence learning"

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)