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: "learning end to end goal oriented dialog with multiple answers"
  Abstract: In a dialog, there can be multiple valid next utterances at any point. The present end-to-end neural methods for dialog do not take this into account. They learn with the assumption that at any time there is only one correct next utterance. In this work, we focus on this problem in the goal-oriented dialog setting where there are different paths to reach a goal. We propose a new method, that uses a combination of supervised learning and reinforcement learning approaches to address this issue. We also propose a new and more effective testbed, permuted-bAbI dialog tasks, by introducing multiple valid next utterances to the original-bAbI dialog tasks, which allows evaluation of goal-oriented dialog systems in a more realistic setting. We show that there is a significant drop in performance of existing end-to-end neural methods from 81.5% per-dialog accuracy on original-bAbI dialog tasks to 30.3% on permuted-bAbI dialog tasks. We also show that our proposed method improves the performance and achieves 47.3% per-dialog accuracy on permuted-bAbI dialog tasks.

Connected through hyperedge HE-1 (induced by source paper "inset sentence infilling with inter sentential generative pre training"):
  with members:
    - "tigs an inference algorithm for text infilling with gradient search"
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "linguistic versus latent relations for modeling coherent flow in paragraphs"
    - "the curious case of neural text degeneration"
    - "what makes a good story designing composite rewards for visual storytelling"
    - "spanbert improving pre training by representing and predicting spans"
    - "abductive reasoning as self supervision for common sense question answering"
    - "sentence bert sentence embeddings using siamese bert networks"

Connected through hyperedge HE-2 (induced by source paper "what makes a good story designing composite rewards for visual storytelling"):
  with members:
    - "uniter learning universal image text representations"
    - "hierarchically structured reinforcement learning for topically coherent visual story generation"
    - "plan and write towards better automatic storytelling"
    - "hybrid retrieval generation reinforced agent for medical image report generation"
    - "learning neural templates for text generation"
    - "towards diverse text generation with inverse reinforcement learning"
    - "no metrics are perfect adversarial reward learning for visual storytelling"
    - "hierarchical neural story generation"

Connected through hyperedge HE-3 (induced by source paper "task oriented dialog systems that consider multiple appropriate responses under the same context"):
  with members:
    - "structured fusion networks for dialog"
    - "the curious case of neural text degeneration"
    - "convlab multi domain end to end dialog system platform"
    - "rethinking action spaces for reinforcement learning in end to end dialog agents with latent variable models"
    - "semantically conditioned dialog response generation via hierarchical disentangled self attention"
    - "hierarchical neural story generation"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "multiwoz a large scale multi domain wizard of oz dataset for task oriented dialogue modelling"

Connected through hyperedge HE-4 (induced by source paper "exploiting persona information for diverse generation of conversational responses"):
  with members:
    - "deepcopy grounded response generation with hierarchical pointer networks"
    - "from eliza to xiaoice challenges and opportunities with social chatbots"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "why are sequence to sequence models so dull understanding the low diversity problem of chatbots"
    - "assigning personality identity to a chatting machine for coherent conversation generation"
    - "learning discourse level diversity for neural dialog models using conditional variational autoencoders"
    - "a persona based neural conversation model"
    - "deep reinforcement learning for dialogue generation"

Connected through hyperedge HE-5 (induced by source paper "learning end to end goal oriented dialog with maximal user task success and minimal human agent use"):
  with members:
    - "dialogue learning with human teaching and feedback in end to end trainable task oriented dialogue systems"
    - "key value retrieval networks for task oriented dialogue"
    - "iterative policy learning in end to end trainable task oriented neural dialog models"
    - "composite task completion dialogue policy learning via hierarchical deep reinforcement learning"
    - "composite task completion dialogue policy learning via hierarchical deep reinforcement learning"
    - "end to end task completion neural dialogue systems"
    - "hybrid code networks practical and efficient end to end dialog control with supervised and reinforcement learning"
    - "a network based end to end trainable task oriented dialogue system"

Connected through hyperedge HE-6 (induced by source paper "what should i ask using conversationally informative rewards for goal oriented visual dialog"):
  with members:
    - "an active information seeking model for goal oriented vision and language tasks"
    - "jointly learning to see ask and guesswhat"
    - "learning goal oriented visual dialog via tempered policy gradient"
    - "question asking as program generation"
    - "best of both worlds transferring knowledge from discriminative learning to a generative visual dialog model"
    - "end to end optimization of goal driven and visually grounded dialogue systems"
    - "guesswhat visual object discovery through multi modal dialogue"
    - "bbq networks efficient exploration in deep reinforcement learning for task oriented dialogue systems"

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)