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: "zero shot learning for semantic utterance classification"
  Abstract: We propose a novel zero-shot learning method for semantic utterance classification (SUC). It learns a classifier $f: X \to Y$ for problems where none of the semantic categories $Y$ are present in the training set. The framework uncovers the link between categories and utterances using a semantic space. We show that this semantic space can be learned by deep neural networks trained on large amounts of search engine query log data. More precisely, we propose a novel method that can learn discriminative semantic features without supervision. It uses the zero-shot learning framework to guide the learning of the semantic features. We demonstrate the effectiveness of the zero-shot semantic learning algorithm on the SUC dataset collected by (Tur, 2012). Furthermore, we achieve state-of-the-art results by combining the semantic features with a supervised method.

Connected through hyperedge HE-1 (induced by source paper "robust zero shot cross domain slot filling with example values"):
  with members:
    - "zero shot adaptive transfer for conversational language understanding"
    - "multi source domain adaptation with mixture of experts"
    - "contextual slot carryover for disparate schemas"
    - "multi task learning for joint language understanding and dialogue state tracking"
    - "decoupling structure and lexicon for zero shot semantic parsing"
    - "snips voice platform an embedded spoken language understanding system for private by design voice interfaces"
    - "fast and scalable expansion of natural language understanding functionality for intelligent agents"
    - "unsupervised transfer learning for spoken language understanding in intelligent agents"

Connected through hyperedge HE-2 (induced by source paper "integrating semantic knowledge to tackle zero shot text classification"):
  with members:
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "a large scale attribute dataset for zero shot learning"
    - "contextual augmentation data augmentation by words with paradigmatic relations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "data augmentation for low resource neural machine translation"
    - "train once test anywhere zero shot learning for text classification"
    - "data noising as smoothing in neural network language models"
    - "zero shot learning a comprehensive evaluation of the good the bad and the ugly"

Connected through hyperedge HE-3 (induced by source paper "an efficient approach to encoding context for spoken language understanding"):
  with members:
    - "building a conversational agent overnight with dialogue self play"
    - "an end to end trainable neural network model with belief tracking for task oriented dialog"
    - "towards end to end reinforcement learning of dialogue agents for information access"
    - "neural belief tracker data driven dialogue state tracking"
    - "attention based recurrent neural network models for joint intent detection and slot filling"
    - "adam a method for stochastic optimization"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

Connected through hyperedge HE-4 (induced by source paper "joint on line learning of a zero shot spoken semantic parser and a reinforcement learning dialogue manager"):
  with members:
    - "zero shot dialog generation with cross domain latent actions"
    - "towards zero shot frame semantic parsing for domain scaling"
    - "end to end task completion neural dialogue systems"
    - "towards end to end reinforcement learning of dialogue agents for information access"
    - "building end to end dialogue systems using generative hierarchical neural network models"
    - "efficient estimation of word representations in vector space"
    - "kalman temporal differences"

Connected through hyperedge HE-5 (induced by source paper "a multiplicative model for learning distributed text based attribute representations"):
  with members:
    - "deepwalk online learning of social representations"
    - "distributed representations of sentences and documents"
    - "an autoencoder approach to learning bilingual word representations"
    - "a convolutional neural network for modelling sentences"
    - "distributed representations of words and phrases and their compositionality"
    - "multilingual distributed representations without word alignment"
    - "a framework for optimizing paper matching"
    - "seeing stars exploiting class relationships for sentiment categorization with respect to rating scales"

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)