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: "functional federated learning in erlang ffl erl"
  Abstract: The functional programming language Erlang is well-suited for concurrent and distributed applications. Numerical computing, however, is not seen as one of its strengths. The recent introduction of Federated Learning, a concept according to which client devices are leveraged for decentralized machine learning tasks, while a central server updates and distributes a global model, provided the motivation for exploring how well Erlang is suited to that problem. We present ffl-erl, a framework for Federated Learning, written in Erlang, and explore how well it performs in two scenarios: one in which the entire system has been written in Erlang, and another in which Erlang is relegated to coordinating client processes that rely on performing numerical computations in the programming language C. There is a concurrent as well as a distributed implementation of each case. Erlang incurs a performance penalty, but for certain use cases this may not be detrimental, considering the trade-off between conciseness of the language and speed of development (Erlang) versus performance (C). Thus, Erlang may be a viable alternative to C for some practical machine learning tasks.

Connected through hyperedge HE-1 (induced by source paper "active code replacement in the oodida data analytics platform"):
  with members:
    - "oodida on board off board distributed data analytics for connected vehicles"
    - "facilitating rapid prototyping in the oodida data analytics platform via active code replacement"
    - "communication efficient learning of deep networks from decentralized data"

Connected through hyperedge HE-2 (induced by source paper "oodida on board off board distributed data analytics for connected vehicles"):
  with members:
    - "distributed deep learning on edge devices feasibility via adaptive compression"
    - "federated optimization distributed machine learning for on device intelligence"

Connected through hyperedge HE-3 (induced by source paper "oodida on board off board distributed real time data analytics for connected vehicles"):
  with members:
    - "distributed deep learning on edge devices feasibility via adaptive compression"
    - "federated learning of deep networks using model averaging"

Connected through hyperedge HE-4 (induced by source paper "leaf a benchmark for federated settings"):
  with members:
    - "improving federated learning personalization via model agnostic meta learning"
    - "federated learning challenges methods and future directions"
    - "federated machine learning concept and applications"
    - "fair resource allocation in federated learning"
    - "efficient decentralized deep learning by dynamic model averaging"
    - "on first order meta learning algorithms"
    - "exploiting unintended feature leakage in collaborative learning"
    - "federated learning strategies for improving communication efficiency"

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.PL(Programming Languages)