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: "biscotti a ledger for private and secure peer to peer machine learning"
  Abstract: Federated Learning is the current state of the art in supporting secure multi-party machine learning (ML): data is maintained on the owner's device and the updates to the model are aggregated through a secure protocol. However, this process assumes a trusted centralized infrastructure for coordination, and clients must trust that the central service does not use the byproducts of client data. In addition to this, a group of malicious clients could also harm the performance of the model by carrying out a poisoning attack. #R##N#As a response, we propose Biscotti: a fully decentralized peer to peer (P2P) approach to multi-party ML, which uses blockchain and cryptographic primitives to coordinate a privacy-preserving ML process between peering clients. Our evaluation demonstrates that Biscotti is scalable, fault tolerant, and defends against known attacks. For example, Biscotti is able to protect the privacy of an individual client's update and the performance of the global model at scale when 30% of adversaries are trying to poison the model. #R##N#The implementation can be found at: this https URL

Connected through hyperedge HE-1 (induced by source paper "on the convergence of artificial intelligence and distributed ledger technology a scoping review and future research agenda"):
  with members:
    - "ekiden a platform for confidentiality preserving trustworthy and performant smart contracts"
    - "energy recycling blockchain with proof of deep learning"
    - "decentralized and collaborative ai on blockchain"
    - "sok transparent dishonesty front running attacks on blockchain"
    - "reliable federated learning for mobile networks"
    - "keystone an open framework for architecting tees"
    - "tensorscone a secure tensorflow framework using intel sgx"
    - "coin ai a proof of useful work scheme for blockchain based distributed deep learning"

Connected through hyperedge HE-2 (induced by source paper "shielding collaborative learning mitigating poisoning attacks through client side detection"):
  with members:
    - "analyzing federated learning through an adversarial lens"
    - "fine pruning defending against backdooring attacks on deep neural networks"
    - "model reuse attacks on deep learning systems"
    - "federated learning strategies for improving communication efficiency"
    - "privacy preserving collaborative deep learning with unreliable participants"
    - "backdooring convolutional neural networks via targeted weight perturbations"
    - "byzantine robust distributed learning towards optimal statistical rates"
    - "how to backdoor federated learning"

Connected through hyperedge HE-3 (induced by source paper "incentive design for efficient federated learning in mobile networks a contract theory approach"):
  with members:
    - "rewarding high quality data via influence functions"
    - "federated machine learning concept and applications"
    - "client selection for federated learning with heterogeneous resources in mobile edge"
    - "efficient training management for mobile crowd machine learning a deep reinforcement learning approach"
    - "federated learning for mobile keyboard prediction"
    - "multi objective evolutionary federated learning"
    - "blockchained on device federated learning"
    - "incentive mechanism design for wireless energy harvesting based internet of things"

Connected through hyperedge HE-4 (induced by source paper "reliable federated learning for mobile networks"):
  with members:
    - "toward intelligent vehicular networks a machine learning framework"
    - "efficient training management for mobile crowd machine learning a deep reinforcement learning approach"
    - "blockchained on device federated learning"
    - "mitigating sybils in federated learning poisoning"
    - "federated learning with non iid 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.LG(Machine Learning)