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: "fedhealth a federated transfer learning framework for wearable healthcare"
  Abstract: With the rapid development of computing technology, wearable devices such as smart phones and wristbands make it easy to get access to people's health information including activities, sleep, sports, etc. Smart healthcare achieves great success by training machine learning models on a large quantity of user data. However, there are two critical challenges. Firstly, user data often exists in the form of isolated islands, making it difficult to perform aggregation without compromising privacy security. Secondly, the models trained on the cloud fail on personalization. In this paper, we propose FedHealth, the first federated transfer learning framework for wearable healthcare to tackle these challenges. FedHealth performs data aggregation through federated learning, and then builds personalized models by transfer learning. It is able to achieve accurate and personalized healthcare without compromising privacy and security. Experiments demonstrate that FedHealth produces higher accuracy (5.3% improvement) for wearable activity recognition when compared to traditional methods. FedHealth is general and extensible and has the potential to be used in many healthcare applications.

Connected through hyperedge HE-1 (induced by source paper "privacy preserving traffic flow prediction a federated learning approach"):
  with members:
    - "towards federated learning at scale system design"
    - "reliable federated learning for mobile networks"
    - "mobile edge computing blockchain and reputation based crowdsourcing iot federated learning a secure decentralized and privacy preserving system"
    - "federated learning challenges methods and future directions"
    - "federated machine learning concept and applications"
    - "client selection for federated learning with heterogeneous resources in mobile edge"
    - "fair resource allocation in federated learning"
    - "a generic framework for privacy preserving deep learning"

Connected through hyperedge HE-2 (induced by source paper "personalized federated learning for intelligent iot applications a cloud edge based framework"):
  with members:
    - "hfel joint edge association and resource allocation for cost efficient hierarchical federated edge learning"
    - "federated learning with personalization layers"
    - "edge intelligence paving the last mile of artificial intelligence with edge computing"
    - "improving federated learning personalization via model agnostic meta learning"
    - "federated learning for healthcare informatics"
    - "wireless federated distillation for distributed edge learning with heterogeneous data"
    - "federated learning challenges methods and future directions"
    - "edge ai on demand accelerating deep neural network inference via edge computing"

Connected through hyperedge HE-3 (induced by source paper "transfer learning with dynamic distribution adaptation"):
  with members:
    - "decomposition based transfer distance metric learning for image classification"
    - "easy transfer learning by exploiting intra domain structures"
    - "transfer learning from speaker verification to multispeaker text to speech synthesis"
    - "importance weighted adversarial nets for partial domain adaptation"
    - "visual domain adaptation with manifold embedded distribution alignment"
    - "cross position activity recognition with stratified transfer learning"
    - "multi adversarial domain adaptation"
    - "joint domain alignment and discriminative feature learning for unsupervised deep domain adaptation"

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)