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: "hierarchical memory networks for answer selection on unknown words"
  Abstract: Recently, end-to-end memory networks have shown promising results on Question Answering task, which encode the past facts into an explicit memory and perform reasoning ability by making multiple computational steps on the memory. However, memory networks conduct the reasoning on sentence-level memory to output coarse semantic vectors and do not further take any attention mechanism to focus on words, which may lead to the model lose some detail information, especially when the answers are rare or unknown words. In this paper, we propose a novel Hierarchical Memory Networks, dubbed HMN. First, we encode the past facts into sentence-level memory and word-level memory respectively. Then, (k)-max pooling is exploited following reasoning module on the sentence-level memory to sample the (k) most relevant sentences to a question and feed these sentences into attention mechanism on the word-level memory to focus the words in the selected sentences. Finally, the prediction is jointly learned over the outputs of the sentence-level reasoning module and the word-level attention mechanism. The experimental results demonstrate that our approach successfully conducts answer selection on unknown words and achieves a better performance than memory networks.

Connected through hyperedge HE-1 (induced by source paper "memorization precedes generation learning unsupervised gans with memory networks"):
  with members:
    - "mcgan mean and covariance feature matching gan"
    - "face aging with conditional generative adversarial networks"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "calibrating energy based generative adversarial networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "improved training of wasserstein gans"
    - "measuring catastrophic forgetting in neural networks"
    - "semi supervised learning with context conditional generative adversarial networks"

Connected through hyperedge HE-2 (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"
    - "a frame tracking model for memory enhanced dialogue systems"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "zero shot visual question answering"):
  with members:
    - "pointing the unknown words"
    - "image captioning and visual question answering based on attributes and external knowledge"
    - "graph structured representations for visual question answering"
    - "visual genome connecting language and vision using crowdsourced dense image annotations"
    - "matching networks for one shot learning"
    - "revisiting visual question answering baselines"
    - "visual question answering a survey of methods and datasets"
    - "multimodal compact bilinear pooling for visual question answering and visual grounding"

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.IR(Information Retrieval)