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: "reply with proactive recommendation of email attachments"
  Abstract: Email responses often contain items---such as a file or a hyperlink to an external document---that are attached to or included inline in the body of the message. Analysis of an enterprise email corpus reveals that 35% of the time when users include these items as part of their response, the attachable item is already present in their inbox or sent folder. A modern email client can proactively retrieve relevant attachable items from the user's past emails based on the context of the current conversation, and recommend them for inclusion, to reduce the time and effort involved in composing the response. In this paper, we propose a weakly supervised learning framework for recommending attachable items to the user. As email search systems are commonly available, we constrain the recommendation task to formulating effective search queries from the context of the conversations. The query is submitted to an existing IR system to retrieve relevant items for attachment. We also present a novel strategy for generating labels from an email corpus---without the need for manual annotations---that can be used to train and evaluate the query formulation model. In addition, we describe a deep convolutional neural network that demonstrates satisfactory performance on this query formulation task when evaluated on the publicly available Avocado dataset and a proprietary dataset of internal emails obtained through an employee participation program.

Connected through hyperedge HE-1 (induced by source paper "neural networks for information retrieval"):
  with members:
    - "neural vector spaces for unsupervised information retrieval"
    - "attentive memory networks efficient machine reading for conversational search"
    - "neural ranking models with weak supervision"
    - "a neural knowledge language model"
    - "learning to attend copy and generate for session based query suggestion"
    - "personalizing session based recommendations with hierarchical recurrent neural networks"
    - "avoiding your teacher s mistakes training neural networks with controlled weak supervision"
    - "deep reinforcement learning for dialogue generation"

Connected through hyperedge HE-2 (induced by source paper "neural vector spaces for unsupervised information retrieval"):
  with members:
    - "neural networks for information retrieval"
    - "semantic entity retrieval toolkit"
    - "efficient vector representation for documents through corruption"
    - "pyndri a python interface to the indri search engine"
    - "a deep relevance matching model for ad hoc retrieval"
    - "learning to match using local and distributed representations of text for web search"
    - "noisy activation functions"
    - "siamese cbow optimizing word embeddings for sentence representations"

Connected through hyperedge HE-3 (induced by source paper "remedies against the vocabulary gap in information retrieval"):
  with members:
    - "neural vector spaces for unsupervised information retrieval"
    - "semantic entity retrieval toolkit"
    - "pyndri a python interface to the indri search engine"
    - "unsupervised efficient and semantic expertise retrieval"
    - "recurrent memory network for language modeling"
    - "learning latent vector spaces for product search"
    - "adadelta an adaptive learning rate method"
    - "the numpy array a structure for efficient numerical computation"

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)