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: "gated end to end memory networks"
  Abstract: Machine reading using differentiable reasoning models has recently shown remarkable progress. In this context, End-to-End trainable Memory Networks, MemN2N, have demonstrated promising performance on simple natural language based reasoning tasks such as factual reasoning and basic deduction. However, other tasks, namely multi-fact question-answering, positional reasoning or dialog related tasks, remain challenging particularly due to the necessity of more complex interactions between the memory and controller modules composing this family of models. In this paper, we introduce a novel end-to-end memory access regulation mechanism inspired by the current progress on the connection short-cutting principle in the field of computer vision. Concretely, we develop a Gated End-to-End trainable Memory Network architecture, GMemN2N. From the machine learning perspective, this new capability is learned in an end-to-end fashion without the use of any additional supervision signal which is, as far as our knowledge goes, the first of its kind. Our experiments show significant improvements on the most challenging tasks in the 20 bAbI dataset, without the use of any domain knowledge. Then, we show improvements on the dialog bAbI tasks including the real human-bot conversion-based Dialog State Tracking Challenge (DSTC-2) dataset. On these two datasets, our model sets the new state of the art.

Connected through hyperedge HE-1 (induced by source paper "a copy augmented sequence to sequence architecture gives good performance on task oriented dialogue"):
  with members:
    - "pointing the unknown words"
    - "a network based end to end trainable task oriented dialogue system"
    - "how not to evaluate your dialogue system an empirical study of unsupervised evaluation metrics for dialogue response generation"
    - "incorporating copying mechanism in sequence to sequence learning"
    - "data recombination for neural semantic parsing"
    - "learning end to end goal oriented dialog"
    - "query reduction networks for question answering"
    - "end to end memory networks"

Connected through hyperedge HE-2 (induced by source paper "key value retrieval networks for task oriented dialogue"):
  with members:
    - "relevance of unsupervised metrics in task oriented dialogue for evaluating natural language generation"
    - "hybrid code networks practical and efficient end to end dialog control with supervised and reinforcement learning"
    - "pointing the unknown words"
    - "a network based end to end trainable task oriented dialogue system"
    - "how not to evaluate your dialogue system an empirical study of unsupervised evaluation metrics for dialogue response generation"
    - "incorporating copying mechanism in sequence to sequence learning"
    - "smart reply automated response suggestion for email"
    - "towards end to end reinforcement learning of dialogue agents for information access"

Connected through hyperedge HE-3 (induced by source paper "scaffolding networks incremental learning and teaching through questioning"):
  with members:
    - "a deep compositional framework for human like language acquisition in virtual environment"
    - "learning language games through interaction"
    - "dialog based language learning"
    - "tracking the world state with recurrent entity networks"
    - "learning to compose neural networks for question answering"
    - "link prediction using embedded knowledge graphs"
    - "learning end to end goal oriented dialog"
    - "query reduction networks for question answering"

Connected through hyperedge HE-4 (induced by source paper "an end to end trainable neural network model with belief tracking for task oriented dialog"):
  with members:
    - "end to end task completion neural dialogue systems"
    - "how not to evaluate your dialogue system an empirical study of unsupervised evaluation metrics for dialogue response generation"
    - "towards end to end learning for dialog state tracking and management using deep reinforcement learning"
    - "neural belief tracker data driven dialogue state tracking"
    - "learning end to end goal oriented dialog"
    - "query reduction networks for question answering"
    - "building end to end dialogue systems using generative hierarchical neural network models"
    - "neural responding machine for short text conversation"

Connected through hyperedge HE-5 (induced by source paper "query reduction networks for question answering"):
  with members:
    - "squad 100 000 questions for machine comprehension of text"
    - "strongly typed recurrent neural networks"
    - "dynamic memory networks for visual and textual question answering"
    - "learning end to end goal oriented dialog"
    - "end to end memory networks"
    - "towards neural network based reasoning"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "towards ai complete question answering a set of prerequisite toy tasks"

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)