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 learning for emoji prediction in a mobile keyboard"
  Abstract: We show that a word-level recurrent neural network can predict emoji from text typed on a mobile keyboard. We demonstrate the usefulness of transfer learning for predicting emoji by pretraining the model using a language modeling task. We also propose mechanisms to trigger emoji and tune the diversity of candidates. The model is trained using a distributed on-device learning framework called federated learning. The federated model is shown to achieve better performance than a server-trained model. This work demonstrates the feasibility of using federated learning to train production-quality models for natural language understanding tasks while keeping users' data on their devices.

Connected through hyperedge HE-1 (induced by source paper "scaffold stochastic controlled averaging for federated learning"):
  with members:
    - "feddane a federated newton type method"
    - "error feedback fixes signsgd and other gradient compression schemes"
    - "distributed learning with compressed gradient differences"
    - "advances and open problems in federated learning"
    - "how good is sgd with random shuffling"
    - "variance reduced local sgd with lower communication complexity"
    - "federated learning of n gram language models"
    - "the error feedback framework better rates for sgd with delayed gradients and compressed communication"

Connected through hyperedge HE-2 (induced by source paper "practical and bilateral privacy preserving federated learning"):
  with members:
    - "skin lesion analysis toward melanoma detection 2018 a challenge hosted by the international skin imaging collaboration isic"
    - "advances and open problems in federated learning"
    - "privacy preserving federated brain tumour segmentation"
    - "communication efficient federated deep learning with asynchronous model update and temporally weighted aggregation"
    - "semi cyclic stochastic gradient descent"
    - "federated learning for healthcare informatics"
    - "federated learning challenges methods and future directions"
    - "agnostic federated learning"

Connected through hyperedge HE-3 (induced by source paper "three approaches for personalization with applications to federated learning"):
  with members:
    - "scaffold stochastic controlled averaging for on device federated learning"
    - "generative models for effective ml on private decentralized datasets"
    - "advances and open problems in federated learning"
    - "improving federated learning personalization via model agnostic meta learning"
    - "federated learning of n gram language models"
    - "federated learning challenges methods and future directions"
    - "federated evaluation of on device personalization"
    - "agnostic federated learning"

Connected through hyperedge HE-4 (induced by source paper "secure federated submodel learning"):
  with members:
    - "behavior sequence transformer for e commerce recommendation in alibaba"
    - "deep leakage from gradients"
    - "towards federated learning at scale system design"
    - "making ai forget you data deletion in machine learning"
    - "federated learning challenges methods and future directions"
    - "agnostic federated learning"
    - "federated machine learning concept and applications"
    - "consistent and accurate frequency oracles under local differential privacy"

Connected through hyperedge HE-5 (induced by source paper "eavesdrop the composition proportion of training labels in federated learning"):
  with members:
    - "distributed federated learning for ultra reliable low latency vehicular communications"
    - "a little is enough circumventing defenses for distributed learning"
    - "updates leak data set inference and reconstruction attacks in online learning"
    - "analyzing federated learning through an adversarial lens"
    - "exploiting unintended feature leakage in collaborative learning"
    - "multi party poisoning through generalized p tampering"
    - "federated learning for ultra reliable low latency v2v communications"
    - "federated learning strategies for improving communication efficiency"

Connected through hyperedge HE-6 (induced by source paper "scaffold stochastic controlled averaging for on device federated learning"):
  with members:
    - "error feedback fixes signsgd and other gradient compression schemes"
    - "distributed learning with compressed gradient differences"
    - "the error feedback framework better rates for sgd with delayed gradients and compressed communication"
    - "communication trade offs for synchronized distributed sgd with large step size"
    - "federated learning challenges methods and future directions"
    - "agnostic federated learning"
    - "first analysis of local gd on heterogeneous data"
    - "federated learning of out of vocabulary words"

Connected through hyperedge HE-7 (induced by source paper "overcoming forgetting in federated learning on non iid data"):
  with members:
    - "robust and communication efficient federated learning from non iid data"
    - "federated optimization for heterogeneous networks"
    - "adaptive federated learning in resource constrained edge computing systems"
    - "federated learning with non iid data"
    - "federated multi task learning"
    - "overcoming catastrophic forgetting in neural networks"
    - "practical secure aggregation for federated learning on user held data"
    - "communication efficient learning of deep networks from decentralized 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.CL(Computation and Language)