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: "attention based hierarchical neural query suggestion"
  Abstract: Query suggestions help users of a search engine to refine their queries. Previous work on query suggestion has mainly focused on incorporating directly observable features such as query co-occurrence and semantic similarity. The structure of such features is often set manually, as a result of which hidden dependencies between queries and users may be ignored. We propose an AHNQS model that combines a hierarchical structure with a session-level neural network and a user-level neural network to model the short- and long-term search history of a user. An attention mechanism is used to capture user preferences. We quantify the improvements of AHNQS over state-of-the-art RNN-based query suggestion baselines on the AOL query log dataset, with improvements of up to 21.86% and 22.99% in terms of MRR@10 and Recall@10, respectively, over the state-of-the-art; improvements are especially large for short sessions.

Connected through hyperedge HE-1 (induced by source paper "a deep look into neural ranking models for information retrieval"):
  with members:
    - "learning groupwise scoring functions using deep neural networks"
    - "simple applications of bert for ad hoc document retrieval"
    - "semeval 2017 task 3 community question answering"
    - "user intent prediction in information seeking conversations"
    - "vitor learning to rank webpages based on visual features"
    - "wikipassageqa a benchmark collection for research on non factoid answer passage retrieval"
    - "anmm ranking short answer texts with attention based neural matching model"
    - "cross domain regularization for neural ranking models using adversarial learning"

Connected through hyperedge HE-2 (induced by source paper "joint neural collaborative filtering for recommender systems"):
  with members:
    - "neural graph collaborative filtering"
    - "deep learning based recommender system a survey and new perspectives"
    - "recurrent neural networks with top k gains for session based recommendations"
    - "neural factorization machines for sparse predictive analytics"
    - "neural collaborative filtering"
    - "fast matrix factorization for online recommendation with implicit feedback"
    - "joint deep modeling of users and items using reviews for recommendation"
    - "deepfm a factorization machine based neural network for ctr prediction"

Connected through hyperedge HE-3 (induced by source paper "context attentive document ranking and query suggestion"):
  with members:
    - "retrieve and read multi task learning of information retrieval and reading comprehension"
    - "a click sequence model for web search"
    - "match tensor a deep relevance model for search"
    - "neural ranking models with weak supervision"
    - "learning to attend copy and generate for session based query suggestion"
    - "a deep relevance matching model for ad hoc retrieval"
    - "learning to match using local and distributed representations of text for web search"
    - "learning natural language inference using bidirectional lstm model and inner attention"

Connected through hyperedge HE-4 (induced by source paper "domain constrained advertising keyword generation"):
  with members:
    - "mojitalk generating emotional responses at scale"
    - "learning discourse level diversity for neural dialog models using conditional variational autoencoders"
    - "a simple fast diverse decoding algorithm for neural generation"
    - "a hierarchical latent variable encoder decoder model for generating dialogues"
    - "a hierarchical recurrent encoder decoder for generative context aware query suggestion"
    - "a term based methodology for query reformulation understanding"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "neural responding machine for short text conversation"

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)