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: "user personalized satisfaction prediction via multiple instance deep learning"
  Abstract: Community based question answering services have arisen as a popular knowledge sharing pattern for netizens. With abundant interactions among users, individuals are capable of obtaining satisfactory information. However, it is not effective for users to attain answers within minutes. Users have to check the progress over time until the satisfying answers submitted. We address this problem as a user personalized satisfaction prediction task. Existing methods usually exploit manual feature selection. It is not desirable as it requires careful design and is labor intensive. In this paper, we settle this issue by developing a new multiple instance deep learning framework. Specifically, in our settings, each question follows a weakly supervised learning multiple instance learning assumption, where its obtained answers can be regarded as instance sets and we define the question resolved with at least one satisfactory answer. We thus design an efficient framework exploiting multiple instance learning property with deep learning to model the question answer pairs. Extensive experiments on large scale datasets from Stack Exchange demonstrate the feasibility of our proposed framework in predicting askers personalized satisfaction. Our framework can be extended to numerous applications such as UI satisfaction Prediction, multi armed bandit problem, expert finding and so on.

Connected through hyperedge HE-1 (induced by source paper "sometimes you want to go where everybody knows your name"):
  with members:
    - "federated learning strategies for improving communication efficiency"
    - "memory aware synapses learning what not to forget"
    - "attend to you personalized image captioning with context sequence memory networks"
    - "measuring catastrophic forgetting in neural networks"
    - "federated multi task learning"
    - "wide deep learning for recommender systems"
    - "overcoming catastrophic forgetting in neural networks"
    - "federated learning of deep networks using model averaging"

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)