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: "robust dialog state tracking for large ontologies"
  Abstract: The Dialog State Tracking Challenge 4 (DSTC 4) differentiates itself from the previous three editions as follows: the number of slot-value pairs present in the ontology is much larger, no spoken language understanding output is given, and utterances are labeled at the subdialog level. This paper describes a novel dialog state tracking method designed to work robustly under these conditions, using elaborate string matching, coreference resolution tailored for dialogs and a few other improvements. The method can correctly identify many values that are not explicitly present in the utterance. On the final evaluation, our method came in first among 7 competing teams and 24 entries. The F1-score achieved by our method was 9 and 7 percentage points higher than that of the runner-up for the utterance-level evaluation and for the subdialog-level evaluation, respectively.

Connected through hyperedge HE-1 (induced by source paper "flexibly structured model for task oriented dialogues"):
  with members:
    - "microsoft dialogue challenge building end to end task completion dialogue systems"
    - "towards universal dialogue state tracking"
    - "an end to end approach for handling unknown slot values in dialogue state tracking"
    - "dialogue learning with human teaching and feedback in end to end trainable task oriented dialogue systems"
    - "mem2seq effectively incorporating knowledge bases into end to end task oriented dialog systems"
    - "sequence to sequence learning for task oriented dialogue with dialogue state representation"
    - "large scale multi domain belief tracking with knowledge sharing"
    - "key value retrieval networks for task oriented dialogue"

Connected through hyperedge HE-2 (induced by source paper "improving dialogue state tracking by discerning the relevant context"):
  with members:
    - "an end to end approach for handling unknown slot values in dialogue state tracking"
    - "toward scalable neural dialogue state tracking model"
    - "a frame tracking model for memory enhanced dialogue systems"
    - "an end to end trainable neural network model with belief tracking for task oriented dialog"
    - "neural belief tracker data driven dialogue state tracking"
    - "incremental lstm based dialog state tracker"
    - "multi domain dialog state tracking using recurrent neural networks"

Connected through hyperedge HE-3 (induced by source paper "scalable multi domain dialogue state tracking"):
  with members:
    - "towards zero shot frame semantic parsing for domain scaling"
    - "a multichannel convolutional neural network for cross language dialog state tracking"
    - "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"
    - "domain adaptation of recurrent neural networks for natural language understanding"
    - "neural belief tracker data driven dialogue state tracking"
    - "multi domain dialog state tracking using recurrent neural networks"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "a multichannel convolutional neural network for cross language dialog state tracking"):
  with members:
    - "character level convolutional networks for text classification"
    - "character aware neural language models"
    - "a sensitivity analysis of and practitioners guide to convolutional neural networks for sentence classification"
    - "convolutional neural networks for sentence classification"
    - "sequence to sequence learning with neural networks"
    - "efficient estimation of word representations in vector space"
    - "improving neural networks by preventing co adaptation of feature detectors"

Connected through hyperedge HE-5 (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"
    - "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"
    - "recurrent polynomial network for dialogue state tracking"
    - "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)