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: "multi task learning for email search ranking with auxiliary query clustering"
  Abstract: User information needs vary significantly across different tasks, and therefore their queries will also differ considerably in their expressiveness and semantics. Many studies have been proposed to model such query diversity by obtaining query types and building query-dependent ranking models. These studies typically require either a labeled query dataset or clicks from multiple users aggregated over the same document. These techniques, however, are not applicable when manual query labeling is not viable, and aggregated clicks are unavailable due to the private nature of the document collection, e.g., in email search scenarios. In this paper, we study how to obtain query type in an unsupervised fashion and how to incorporate this information into query-dependent ranking models. We first develop a hierarchical clustering algorithm based on truncated SVD and varimax rotation to obtain coarse-to-fine query types. Then, we study three query-dependent ranking models, including two neural models that leverage query type information as additional features, and one novel multi-task neural model that views query type as the label for the auxiliary query cluster prediction task. This multi-task model is trained to simultaneously rank documents and predict query types. Our experiments on tens of millions of real-world email search queries demonstrate that the proposed multi-task model can significantly outperform the baseline neural ranking models, which either do not incorporate query type information or just simply feed query type as an additional feature.

Connected through hyperedge HE-1 (induced by source paper "separate and attend in personal email search"):
  with members:
    - "spherical text embedding"
    - "tf ranking scalable tensorflow library for learning to rank"
    - "area attention"
    - "domain adaptation for enterprise email search"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "deeprank a new deep architecture for relevance ranking in information retrieval"
    - "attention is all you need"
    - "neural ranking models with weak supervision"

Connected through hyperedge HE-2 (induced by source paper "domain adaptation for enterprise email search"):
  with members:
    - "tf ranking scalable tensorflow library for learning to rank"
    - "cross domain regularization for neural ranking models using adversarial learning"
    - "neural ranking models with weak supervision"
    - "adversarial discriminative domain adaptation"
    - "stabilizing training of generative adversarial networks through regularization"
    - "improved training of wasserstein gans"
    - "neural models for information retrieval"
    - "deep reconstruction classification networks for unsupervised domain adaptation"

Connected through hyperedge HE-3 (induced by source paper "time weighted attentional session aware recommender system"):
  with members:
    - "towards neural mixture recommender for long range dependent user sequences"
    - "sequence aware recommender systems"
    - "recurrent neural networks for long and short term sequential recommendation"
    - "recurrent neural networks with top k gains for session based recommendations"
    - "weakly supervised hierarchical text classification"
    - "mining entity synonyms with efficient neural set generation"
    - "session aware information embedding for e commerce product recommendation"
    - "improving session recommendation with recurrent neural networks by exploiting dwell time"

Connected through hyperedge HE-4 (induced by source paper "boosting search performance using query variations"):
  with members:
    - "query driven algorithm selection in early stage retrieval"
    - "compressing graphs and indexes with recursive graph bisection"

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)