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: "recurrent polynomial network for dialogue state tracking"
  Abstract: Dialogue state tracking (DST) is a process to estimate the distribution of the dialogue states as a dialogue progresses. Recent studies on constrained Markov Bayesian polynomial (CMBP) framework take the first step towards bridging the gap between rule-based and statistical approaches for DST. In this paper, the gap is further bridged by a novel framework -- recurrent polynomial network (RPN). RPN's unique structure enables the framework to have all the advantages of CMBP including efficiency, portability and interpretability. Additionally, RPN achieves more properties of statistical approaches than CMBP. RPN was evaluated on the data corpora of the second and the third Dialog State Tracking Challenge (DSTC-2/3). Experiments showed that RPN can significantly outperform both traditional rule-based approaches and statistical approaches with similar feature set. Compared with the state-of-the-art statistical DST approaches with a lot richer features, RPN is also competitive.

Connected through hyperedge HE-1 (induced by source paper "scalable neural dialogue state tracking"):
  with members:
    - "towards universal dialogue state tracking"
    - "toward scalable neural dialogue state tracking model"
    - "end to end task completion neural dialogue systems"
    - "reporting score distributions makes a difference performance study of lstm networks for sequence tagging"
    - "an end to end trainable neural network model with belief tracking for task oriented dialog"
    - "a network based end to end trainable task oriented dialogue system"
    - "a joint many task model growing a neural network for multiple nlp tasks"
    - "neural belief tracker data driven dialogue state tracking"

Connected through hyperedge HE-2 (induced by source paper "neural user simulation for corpus based policy optimisation for spoken dialogue systems"):
  with members:
    - "sample efficient deep reinforcement learning for dialogue systems with large action spaces"
    - "building a conversational agent overnight with dialogue self play"
    - "large scale multi domain belief tracking with knowledge sharing"
    - "iterative policy learning in end to end trainable task oriented neural dialog models"
    - "a sequence to sequence model for user simulation in spoken dialogue systems"
    - "neural belief tracker data driven dialogue state tracking"
    - "incremental lstm based dialog state tracker"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "towards universal dialogue state tracking"):
  with members:
    - "an end to end approach for handling unknown slot values in dialogue state tracking"
    - "large scale multi domain belief tracking with knowledge sharing"
    - "scalable multi domain dialogue state tracking"
    - "neural belief tracker data driven dialogue state tracking"
    - "dialog state tracking a machine reading approach using memory network"

Connected through hyperedge HE-4 (induced by source paper "fully statistical neural belief tracking"):
  with members:
    - "sample efficient actor critic reinforcement learning with supervised data for dialogue management"
    - "adam a method for stochastic optimization"
    - "simlex 999 evaluating semantic models with genuine similarity estimation"

Connected through hyperedge HE-5 (induced by source paper "multimodal polynomial fusion for detecting driver distraction"):
  with members:
    - "multimodal machine learning a survey and taxonomy"
    - "what can be predicted from six seconds of driver glances"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "neural belief tracker data driven dialogue state tracking"):
  with members:
    - "hybrid dialog state tracker with asr features"
    - "joint online spoken language understanding and language modeling with recurrent neural networks"
    - "robust dialog state tracking for large ontologies"
    - "spectral decomposition method of dialog state tracking via collective matrix factorization"
    - "dialog state tracking a machine reading approach using memory network"
    - "attention based recurrent neural network models for joint intent detection and slot filling"
    - "incremental lstm based dialog state tracker"
    - "recurrent neural networks with external memory for language understanding"

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)