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: "federated tensor factorization for computational phenotyping"
  Abstract: Tensor factorization models offer an effective approach to convert massive electronic health records into meaningful clinical concepts (phenotypes) for data analysis. These models need a large amount of diverse samples to avoid population bias. An open challenge is how to derive phenotypes jointly across multiple hospitals, in which direct patient-level data sharing is not possible (e.g., due to institutional policies). In this paper, we developed a novel solution to enable federated tensor factorization for computational phenotyping without sharing patient-level data. We developed secure data harmonization and federated computation procedures based on alternating direction method of multipliers (ADMM). Using this method, the multiple hospitals iteratively update tensors and transfer secure summarized information to a central server, and the server aggregates the information to generate phenotypes. We demonstrated with real medical datasets that our method resembles the centralized training model (based on combined datasets) in terms of accuracy and phenotypes discovery while respecting privacy.

Connected through hyperedge HE-1 (induced by source paper "federating recommendations using differentially private prototypes"):
  with members:
    - "advances and open problems in federated learning"
    - "federated collaborative filtering for privacy preserving personalized recommendation system"
    - "practical differentially private top k selection with pay what you get composition"
    - "applied federated learning improving google keyboard query suggestions"
    - "a general approach to adding differential privacy to iterative training procedures"
    - "federated learning for mobile keyboard prediction"
    - "deep models under the gan information leakage from collaborative deep learning"
    - "bolt on differential privacy for scalable stochastic gradient descent based analytics"

Connected through hyperedge HE-2 (induced by source paper "privacy preserving tensor factorization for collaborative health data analysis"):
  with members:
    - "differentially private model publishing for deep learning"
    - "deep models under the gan information leakage from collaborative deep learning"
    - "concentrated differential privacy simplifications extensions and lower bounds"
    - "bolt on differential privacy for scalable stochastic gradient descent based analytics"
    - "concentrated differential privacy"
    - "membership inference attacks against machine learning models"
    - "fast differentially private matrix factorization"
    - "deep learning with elastic averaging sgd"

Connected through hyperedge HE-3 (induced by source paper "learning private neural language modeling with attentive aggregation"):
  with members:
    - "on first order meta learning algorithms"
    - "federated learning strategies for improving communication efficiency"
    - "federated meta learning for recommendation"
    - "federated meta learning with fast convergence and efficient communication"
    - "differentially private federated learning a client level perspective"
    - "terngrad ternary gradients to reduce communication in distributed deep learning"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "federated multi task learning"

Connected through hyperedge HE-4 (induced by source paper "copa constrained parafac2 for sparse large datasets"):
  with members:
    - "spartan scalable parafac2 for large sparse data"

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.LG(Machine Learning)