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: "secure federated transfer learning"
  Abstract: Machine learning relies on the availability of a vast amount of data for training. However, in reality, most data are scattered across different organizations and cannot be easily integrated under many legal and practical constraints. In this paper, we introduce a new technique and framework, known as federated transfer learning (FTL), to improve statistical models under a data federation. The federation allows knowledge to be shared without compromising user privacy, and enables complimentary knowledge to be transferred in the network. As a result, a target-domain party can build more flexible and powerful models by leveraging rich labels from a source-domain party. A secure transfer cross validation approach is also proposed to guard the FTL performance under the federation. The framework requires minimal modifications to the existing model structure and provides the same level of accuracy as the non-privacy-preserving approach. This framework is very flexible and can be effectively adapted to various secure multi-party machine learning tasks.

Connected through hyperedge HE-1 (induced by source paper "neural architecture search over decentralized data"):
  with members:
    - "towards federated learning at scale system design"
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "deepcache principled cache for mobile deep vision"
    - "federated learning strategies for improving communication efficiency"
    - "leaf a benchmark for federated settings"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "path level network transformation for efficient architecture search"
    - "federated learning for mobile keyboard prediction"

Connected through hyperedge HE-2 (induced by source paper "a survey towards federated semi supervised learning"):
  with members:
    - "federated learning with matched averaging"
    - "advances and open problems in federated learning"
    - "adaptive image feature learning for disease classification using inductive graph networks"
    - "unsupervised data augmentation for consistency training"
    - "federated machine learning concept and applications"
    - "mixmatch a holistic approach to semi supervised learning"
    - "federated adversarial domain adaptation"
    - "secureboost a lossless federated learning framework"

Connected through hyperedge HE-3 (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-4 (induced by source paper "fedvision an online visual object detection platform powered by federated learning"):
  with members:
    - "towards federated learning at scale system design"
    - "federated machine learning concept and applications"
    - "federated learning strategies for improving communication efficiency"
    - "yolov3 an incremental improvement"
    - "object detection with deep learning a review"
    - "building ethics into artificial intelligence"
    - "a survey of model compression and acceleration for deep neural networks"
    - "federated learning of deep networks using model averaging"

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"
    - "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-6 (induced by source paper "distributed machine learning on mobile devices a survey"):
  with members:
    - "towards federated learning at scale system design"
    - "robust and communication efficient federated learning from non iid data"
    - "on the convergence of adam and beyond"
    - "federated machine learning concept and applications"
    - "secureboost a lossless federated learning framework"
    - "federated learning of out of vocabulary words"
    - "applied federated learning improving google keyboard query suggestions"
    - "federated learning strategies for improving communication efficiency"

Connected through hyperedge HE-7 (induced by source paper "federated adversarial domain adaptation"):
  with members:
    - "domain agnostic learning with disentangled representations"
    - "moment matching for multi source domain adaptation"
    - "a unified feature disentangler for multi domain image translation and manipulation"
    - "cycada cycle consistent adversarial domain adaptation"
    - "maximum classifier discrepancy for unsupervised domain adaptation"
    - "synthetic to real adaptation with generative correlation alignment networks"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "adversarial discriminative domain adaptation"

Connected through hyperedge HE-8 (induced by source paper "federated user representation learning"):
  with members:
    - "federated collaborative filtering for privacy preserving personalized recommendation system"
    - "towards federated learning at scale system design"
    - "federated learning challenges methods and future directions"
    - "federated machine learning concept and applications"
    - "fair resource allocation in federated learning"
    - "pytorch biggraph a large scale graph embedding system"
    - "federated learning strategies for improving communication efficiency"
    - "federated optimization for heterogeneous networks"

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)