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: "a frame tracking model for memory enhanced dialogue systems"
  Abstract: Recently, resources and tasks were proposed to go beyond state tracking in dialogue systems. An example is the frame tracking task, which requires recording multiple frames, one for each user goal set during the dialogue. This allows a user, for instance, to compare items corresponding to different goals. This paper proposes a model which takes as input the list of frames created so far during the dialogue, the current user utterance as well as the dialogue acts, slot types, and slot values associated with this utterance. The model then outputs the frame being referenced by each triple of dialogue act, slot type, and slot value. We show that on the recently published Frames dataset, this model significantly outperforms a previously proposed rule-based baseline. In addition, we propose an extensive analysis of the frame tracking task by dividing it into sub-tasks and assessing their difficulty with respect to our model.

Connected through hyperedge HE-1 (induced by source paper "deep reinforcement learning for modeling chit chat dialog with discrete attributes"):
  with members:
    - "an analysis of neural language modeling at multiple scales"
    - "emotional chatting machine emotional conversation generation with internal and external memory"
    - "natural language does not emerge naturally in multi agent dialog"
    - "regularizing and optimizing lstm language models"
    - "why do neural dialog systems generate short and meaningless replies a comparison between dialog and translation"
    - "a conditional variational framework for dialog generation"
    - "deal or no deal end to end learning for negotiation dialogues"
    - "learning discourse level diversity for neural dialog models using conditional variational autoencoders"

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"
    - "an end to end trainable neural network model with belief tracking for task oriented dialog"
    - "robust dialog state tracking for large ontologies"
    - "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 "addressing objects and their relations the conversational entity dialogue model"):
  with members:
    - "sample efficient actor critic reinforcement learning with supervised data for dialogue management"
    - "composite task completion dialogue policy learning via hierarchical deep reinforcement learning"
    - "composite task completion dialogue policy learning via hierarchical deep reinforcement learning"
    - "hybrid code networks practical and efficient end to end dialog control with supervised and reinforcement learning"
    - "deep reinforcement learning for dialogue generation"
    - "building end to end dialogue systems using generative hierarchical neural network models"

Connected through hyperedge HE-4 (induced by source paper "multi level memory for task oriented dialogs"):
  with members:
    - "subgoal discovery for hierarchical dialogue policy learning"
    - "mem2seq effectively incorporating knowledge bases into end to end task oriented dialog systems"
    - "relevance of unsupervised metrics in task oriented dialogue for evaluating natural language generation"
    - "key value retrieval networks for task oriented dialogue"
    - "composite task completion dialogue policy learning via hierarchical deep reinforcement learning"
    - "frames a corpus for adding memory to goal oriented dialogue systems"
    - "composite task completion dialogue policy learning via hierarchical deep reinforcement learning"
    - "pointing the unknown words"

Connected through hyperedge HE-5 (induced by source paper "contextual slot carryover for disparate schemas"):
  with members:
    - "just ask building an architecture for extensible self service spoken language understanding"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "ld sds towards an expressive spoken dialogue system based on linked data"):
  with members:
    - "hybrid code networks practical and efficient end to end dialog control with supervised and reinforcement learning"
    - "language generation with recurrent generative adversarial networks without pre training"
    - "a network based end to end trainable task oriented dialogue system"
    - "multi domain neural network language generation for spoken dialogue systems"
    - "neural text generation from structured data with application to the biography domain"
    - "dialogue manager domain adaptation using gaussian process reinforcement learning"
    - "natural language generation enhances human decision making with uncertain information"
    - "end to end joint learning of natural language understanding and dialogue manager"

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)