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: "exploiting sentence and context representations in deep neural models for spoken language understanding"
  Abstract: This paper presents a deep learning architecture for the semantic decoder component of a Statistical Spoken Dialogue System. In a slot-filling dialogue, the semantic decoder predicts the dialogue act and a set of slot-value pairs from a set of n-best hypotheses returned by the Automatic Speech Recognition. Most current models for spoken language understanding assume (i) word-aligned semantic annotations as in sequence taggers and (ii) delexicalisation, or a mapping of input words to domain-specific concepts using heuristics that try to capture morphological variation but that do not scale to other domains nor to language variation (e.g., morphology, synonyms, paraphrasing ). In this work the semantic decoder is trained using unaligned semantic annotations and it uses distributed semantic representation learning to overcome the limitations of explicit delexicalisation. The proposed architecture uses a convolutional neural network for the sentence representation and a long-short term memory network for the context representation. Results are presented for the publicly available DSTC2 corpus and an In-car corpus which is similar to DSTC2 but has a significantly higher word error rate (WER).

Connected through hyperedge HE-1 (induced by source paper "collaborative multi agent dialogue model training via reinforcement learning"):
  with members:
    - "textworld a learning environment for text based games"
    - "switch based active deep dyna q efficient adaptive planning for task completion dialogue policy learning"
    - "neural approaches to conversational ai"
    - "counterfactual multi agent policy gradients"
    - "sample efficient actor critic reinforcement learning with supervised data for dialogue management"
    - "iterative policy learning in end to end trainable task oriented neural dialog models"
    - "end to end task completion neural dialogue systems"
    - "hybrid code networks practical and efficient end to end dialog control with supervised and reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "dialogue act classification in group chats with dag lstms"):
  with members:
    - "tree to tree neural networks for program translation"
    - "deep dialog act recognition using multiple token segment and context information representations"
    - "dailydialog a manually labelled multi turn dialogue dataset"
    - "jointly learning sentence embeddings and syntax with unsupervised tree lstms"
    - "dag based long short term memory for neural word segmentation"
    - "towards end to end learning for dialog state tracking and management using deep reinforcement learning"
    - "enhanced lstm for natural language inference"
    - "the influence of context on dialogue act recognition"

Connected through hyperedge HE-3 (induced by source paper "midas a dialog act annotation scheme for open domain human machine spoken conversations"):
  with members:
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "contextual topic modeling for dialog systems"
    - "unsupervised transfer learning for spoken language understanding in intelligent agents"
    - "iso standard domain independent dialogue act tagging for conversational agents"
    - "advances in pre training distributed word representations"
    - "effective approaches to attention based neural machine translation"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "a hierarchical decoding model for spoken language understanding from unaligned data"):
  with members:
    - "get to the point summarization with pointer generator networks"
    - "pointing the unknown words"
    - "using the output embedding to improve language models"
    - "effective approaches to attention based neural machine translation"

Connected through hyperedge HE-5 (induced by source paper "deep learning for language understanding of mental health concepts derived from cognitive behavioural therapy"):
  with members:
    - "hybrid code networks practical and efficient end to end dialog control with supervised and reinforcement learning"
    - "deep reinforcement learning for dialogue generation"
    - "policy networks with two stage training for dialogue systems"
    - "neural belief tracker data driven dialogue state tracking"
    - "skip thought vectors"
    - "distributed representations of sentences and documents"
    - "convolutional neural networks for sentence classification"
    - "a convolutional neural network for modelling sentences"

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.AI(Artificial Intelligence)