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: "private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption"
  Abstract: Consider two data providers, each maintaining private records of different feature sets about common entities. They aim to learn a linear model jointly in a federated setting, namely, data is local and a shared model is trained from locally computed updates. In contrast with most work on distributed learning, in this scenario (i) data is split vertically, i.e. by features, (ii) only one data provider knows the target variable and (iii) entities are not linked across the data providers. Hence, to the challenge of private learning, we add the potentially negative consequences of mistakes in entity resolution. Our contribution is twofold. First, we describe a three-party end-to-end solution in two phases ---privacy-preserving entity resolution and federated logistic regression over messages encrypted with an additively homomorphic scheme---, secure against a honest-but-curious adversary. The system allows learning without either exposing data in the clear or sharing which entities the data providers have in common. Our implementation is as accurate as a naive non-private solution that brings all data in one place, and scales to problems with millions of entities with hundreds of features. Second, we provide what is to our knowledge the first formal analysis of the impact of entity resolution's mistakes on learning, with results on how optimal classifiers, empirical losses, margins and generalisation abilities are affected. Our results bring a clear and strong support for federated learning: under reasonable assumptions on the number and magnitude of entity resolution's mistakes, it can be extremely beneficial to carry out federated learning in the setting where each peer's data provides a significant uplift to the other.

Connected through hyperedge HE-1 (induced by source paper "multi participant multi class vertical federated learning"):
  with members:
    - "parallel distributed logistic regression for vertical federated learning without third party coordinator"
    - "measure contribution of participants in federated learning"
    - "advances and open problems in federated learning"
    - "a communication efficient vertical federated learning framework"
    - "a quasi newton method based vertical federated learning framework for logistic regression"
    - "federated machine learning concept and applications"
    - "entity resolution and federated learning get a federated resolution"
    - "feature selection a data perspective"

Connected through hyperedge HE-2 (induced by source paper "federated extra trees with privacy preserving"):
  with members:
    - "federated forest"
    - "local differential privacy for deep learning"
    - "federated machine learning concept and applications"
    - "secureboost a lossless federated learning framework"
    - "federated learning strategies for improving communication efficiency"
    - "loadaboost loss based adaboost federated machine learning on medical data"
    - "multi institutional deep learning modeling without sharing patient data a feasibility study on brain tumor segmentation"
    - "differential private stack generalization with an application to diabetes prediction"

Connected through hyperedge HE-3 (induced by source paper "decentralized policy based private analytics"):
  with members:
    - "advances and open problems in federated learning"
    - "towards federated learning at scale system design"
    - "distributed training with heterogeneous data bridging median and mean based algorithms"
    - "conclave secure multi party computation on big data"
    - "analyzing federated learning through an adversarial lens"
    - "federated learning strategies for improving communication efficiency"
    - "federated learning for mobile keyboard prediction"
    - "how to backdoor federated learning"

Connected through hyperedge HE-4 (induced by source paper "industrial scale privacy preserving deep neural network"):
  with members:
    - "privacy preserving pca for multiparty modeling"
    - "privacy preserving point of interest recommendation using decentralized matrix factorization"
    - "secure social recommendation based on secret sharing"
    - "practical privacy preserving poi recommendation"
    - "split learning for health distributed deep learning without sharing raw patient data"
    - "distributed learning of deep neural network over multiple agents"
    - "securing input data of deep learning inference systems via partitioned enclave execution"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-5 (induced by source paper "knowledge federation hierarchy and unification"):
  with members:
    - "federated machine learning concept and applications"
    - "cryptonn training neural networks over encrypted data"
    - "federated learning strategies for improving communication efficiency"
    - "entity resolution and federated learning get a federated resolution"
    - "secure federated transfer learning"
    - "communication efficient learning of deep networks from decentralized data"
    - "from data fusion to knowledge fusion"

Connected through hyperedge HE-6 (induced by source paper "privacy preserving weighted federated learning within oracle aided mpc framework"):
  with members:
    - "extending relational query processing with ml inference"
    - "mlsys the new frontier of machine learning systems"
    - "towards federated learning at scale system design"
    - "cloudy with high chance of dbms a 10 year prediction for enterprise grade ml"
    - "federated machine learning concept and applications"
    - "entity resolution and federated learning get a federated resolution"
    - "communication efficient learning of deep networks from decentralized data"

Connected through hyperedge HE-7 (induced by source paper "privacy preserving pca for multiparty modeling"):
  with members:
    - "privacy preserving point of interest recommendation using decentralized matrix factorization"
    - "practical privacy preserving poi recommendation"
    - "federated pca with adaptive rank estimation"

Connected through hyperedge HE-8 (induced by source paper "robust and communication efficient federated learning from non iid data"):
  with members:
    - "towards federated learning at scale system design"
    - "deep neural networks for no reference and full reference image quality assessment"
    - "compact and computationally efficient representation of deep neural networks"
    - "signsgd with majority vote is communication efficient and fault tolerant"
    - "federated learning strategies for improving communication efficiency"
    - "sparse binary compression towards distributed deep learning with minimal communication"
    - "variance based gradient compression for efficient distributed deep learning"
    - "speech commands a dataset for limited vocabulary speech recognition"

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)