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: "entity resolution and federated learning get a federated resolution"
  Abstract: Consider two data providers, each maintaining records of different feature sets about common entities. They aim to learn a linear model over the whole set of features. This problem of federated learning over vertically partitioned data includes a crucial upstream issue: entity resolution, i.e. finding the correspondence between the rows of the datasets. It is well known that entity resolution, just like learning, is mistake-prone in the real world. Despite the importance of the problem, there has been no formal assessment of how errors in entity resolution impact learning. #R##N#In this paper, we provide a thorough answer to this question, answering how optimal classifiers, empirical losses, margins and generalisation abilities are affected. While our answer spans a wide set of losses --- going beyond proper, convex, or classification calibrated ---, it brings simple practical arguments to upgrade entity resolution as a preprocessing step to learning. One of these suggests that entity resolution should be aimed at controlling or minimizing the number of matching errors between examples of distinct classes. In our experiments, we modify a simple token-based entity resolution algorithm so that it indeed aims at avoiding matching rows belonging to different classes, and perform experiments in the setting where entity resolution relies on noisy data, which is very relevant to real world domains. Notably, our approach covers the case where one peer \textit{does not} have classes, or a noisy record of classes. Experiments display that using the class information during entity resolution can buy significant uplift for learning at little expense from the complexity standpoint.

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"
    - "feature selection a data perspective"
    - "private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption"

Connected through hyperedge HE-2 (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"
    - "secure federated transfer learning"
    - "private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption"
    - "communication efficient learning of deep networks from decentralized data"
    - "from data fusion to knowledge fusion"

Connected through hyperedge HE-3 (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"
    - "private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption"
    - "communication efficient learning of deep networks from decentralized data"

Connected through hyperedge HE-4 (induced by source paper "federated machine learning concept and applications"):
  with members:
    - "on device federated learning via blockchain and its latency analysis"
    - "exploiting unintended feature leakage in collaborative learning"
    - "federated learning strategies for improving communication efficiency"
    - "securing distributed machine learning in high dimensions"
    - "how to backdoor federated learning"
    - "chameleon a hybrid secure computation framework for machine learning applications"
    - "federated meta learning for recommendation"
    - "federated learning with non iid data"

Connected through hyperedge HE-5 (induced by source paper "federated forest"):
  with members:
    - "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"
    - "leaf a benchmark for federated settings"
    - "federated meta learning for recommendation"
    - "secure federated transfer learning"
    - "differentially private federated learning a client level perspective"

Connected through hyperedge HE-6 (induced by source paper "boosting privately privacy preserving federated extreme boosting for mobile crowdsensing"):
  with members:
    - "analyzing gender inequality through large scale facebook advertising data"
    - "federated machine learning concept and applications"
    - "federated learning for ultra reliable low latency v2v communications"
    - "beyond inferring class representatives user level privacy leakage from federated learning"
    - "xgboost a scalable tree boosting system"
    - "communication efficient learning of deep networks from decentralized data"

Connected through hyperedge HE-7 (induced by source paper "secure and efficient federated transfer learning"):
  with members:
    - "helen maliciously secure coopetitive learning for linear models"
    - "secure computation for machine learning with spdz"
    - "secure federated transfer learning"
    - "federated learning of deep networks using model averaging"
    - "membership inference attacks against machine learning models"

Connected through hyperedge HE-8 (induced by source paper "secure federated transfer learning"):
  with members:
    - "private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption"
    - "a survey on homomorphic encryption schemes theory and implementation"

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.DB(Databases)