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: "microsoft dialogue challenge building end to end task completion dialogue systems"
  Abstract: This proposal introduces a Dialogue Challenge for building end-to-end task-completion dialogue systems, with the goal of encouraging the dialogue research community to collaborate and benchmark on standard datasets and unified experimental environment. In this special session, we will release human-annotated conversational data in three domains (movie-ticket booking, restaurant reservation, and taxi booking), as well as an experiment platform with built-in simulators in each domain, for training and evaluation purposes. The final submitted systems will be evaluated both in simulated setting and by human judges.

Connected through hyperedge HE-1 (induced by source paper "training neural response selection for task oriented dialogue systems"):
  with members:
    - "passage re ranking with bert"
    - "semeval 2015 task 3 answer selection in community question answering"
    - "simple applications of bert for ad hoc document retrieval"
    - "a repository of conversational datasets"
    - "multi task deep neural networks for natural language understanding"
    - "bert post training for review reading comprehension and aspect based sentiment analysis"
    - "learning semantic textual similarity from conversations"
    - "improving response selection in multi turn dialogue systems by incorporating domain knowledge"

Connected through hyperedge HE-2 (induced by source paper "a repository of conversational datasets"):
  with members:
    - "designing for health chatbots"
    - "training neural response selection for task oriented dialogue systems"
    - "learning semantic textual similarity from conversations"
    - "improving response selection in multi turn dialogue systems by incorporating domain knowledge"
    - "fully statistical neural belief tracking"
    - "deep contextualized word representations"
    - "universal sentence encoder"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-3 (induced by source paper "flexibly structured model for task oriented dialogues"):
  with members:
    - "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"
    - "scalable multi domain dialogue state tracking"

Connected through hyperedge HE-4 (induced by source paper "budgeted policy learning for task oriented dialogue systems"):
  with members:
    - "dialogue learning with human teaching and feedback in end to end trainable task oriented dialogue systems"
    - "switch based active deep dyna q efficient adaptive planning for task completion dialogue policy learning"
    - "integrating planning for task completion dialogue policy learning"
    - "neural approaches to conversational ai"
    - "discriminative deep dyna q robust planning for dialogue policy learning"
    - "iterative policy learning in end to end trainable task oriented neural dialog models"
    - "a tutorial on thompson sampling"
    - "end to end task completion neural dialogue systems"

Connected through hyperedge HE-5 (induced by source paper "convlab multi domain end to end dialog system platform"):
  with members:
    - "user modeling for task oriented dialogues"
    - "onenet joint domain intent slot prediction for spoken language understanding"
    - "self imitation learning"
    - "mem2seq effectively incorporating knowledge bases into end to end task oriented dialog systems"
    - "neural approaches to conversational ai"
    - "multiwoz a large scale multi domain wizard of oz dataset for task oriented dialogue modelling"
    - "large scale multi domain belief tracking with knowledge sharing"
    - "neural user simulation for corpus based policy optimisation for spoken dialogue systems"

Connected through hyperedge HE-6 (induced by source paper "transfer learning for context aware spoken language understanding"):
  with members:
    - "zero shot adaptive transfer for conversational language understanding"
    - "joint slot filling and intent detection via capsule neural networks"
    - "deep contextualized word representations"
    - "an efficient approach to encoding context for spoken language understanding"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "snips voice platform an embedded spoken language understanding system for private by design voice interfaces"
    - "multiwoz a large scale multi domain wizard of oz dataset for task oriented dialogue modelling"
    - "unsupervised transfer learning for spoken language understanding in intelligent agents"

Connected through hyperedge HE-7 (induced by source paper "machines getting with the program understanding intent arguments of non canonical directives"):
  with members:
    - "bertscore evaluating text generation with bert"
    - "disambiguating speech intention via audio text co attention framework a case of prosody semantics interface"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "extracting arguments from korean question and command an annotated corpus for structured paraphrasing"
    - "speech intention understanding in a head final language a disambiguation utilizing intonation dependency"
    - "learning to paraphrase for question answering"
    - "attention is all you need"
    - "cross domain semantic parsing via paraphrasing"

Connected through hyperedge HE-8 (induced by source paper "modeling multi action policy for task oriented dialogues"):
  with members:
    - "user modeling for task oriented dialogues"
    - "recurrently controlled recurrent networks"
    - "sample efficient actor critic reinforcement learning with supervised data for dialogue management"
    - "latent intention dialogue models"
    - "hybrid code networks practical and efficient end to end dialog control with supervised and reinforcement learning"
    - "incorporating copying mechanism in sequence to sequence learning"
    - "end to end lstm based dialog control optimized with supervised and reinforcement learning"
    - "adam a method for stochastic optimization"

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)