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: "a little is enough circumventing defenses for distributed learning"
  Abstract: Distributed learning is central for large-scale training of deep-learning models. However, they are exposed to a security threat in which Byzantine participants can interrupt or control the learning process. Previous attack models and their corresponding defenses assume that the rogue participants are (a) omniscient (know the data of all other participants), and (b) introduce large change to the parameters. We show that small but well-crafted changes are sufficient, leading to a novel non-omniscient attack on distributed learning that go undetected by all existing defenses. We demonstrate our attack method works not only for preventing convergence but also for repurposing of the model behavior (backdooring). We show that 20% of corrupt workers are sufficient to degrade a CIFAR10 model accuracy by 50%, as well as to introduce backdoors into MNIST and CIFAR10 models without hurting their accuracy

Connected through hyperedge HE-1 (induced by source paper "towards byzantine resilient learning in decentralized systems"):
  with members:
    - "zeno robust fully asynchronous sgd"
    - "slsgd secure and efficient distributed on device machine learning"
    - "towards federated learning at scale system design"
    - "e2 train training state of the art cnns with over 80 energy savings"
    - "zeno robust asynchronous sgd with arbitrary number of byzantine workers"
    - "local model poisoning attacks to byzantine robust federated learning"
    - "communication compression for decentralized training"
    - "generalized byzantine tolerant sgd"

Connected through hyperedge HE-2 (induced by source paper "distributed momentum for byzantine resilient learning"):
  with members:
    - "detox a redundancy based framework for faster and more robust gradient aggregation"
    - "can you really backdoor federated learning"
    - "sgd decentralized byzantine resilience"
    - "momentum based variance reduction in non convex sgd"
    - "byzantine stochastic gradient descent"
    - "signsgd with majority vote is communication efficient and fault tolerant"
    - "generalized byzantine tolerant sgd"
    - "byzantine robust distributed learning towards optimal statistical rates"

Connected through hyperedge HE-3 (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"
    - "updates leak data set inference and reconstruction attacks in online learning"
    - "federated learning for emoji prediction in a mobile keyboard"
    - "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-4 (induced by source paper "bypassing backdoor detection algorithms in deep learning"):
  with members:
    - "detecting backdoor attacks on deep neural networks by activation clustering"
    - "fine pruning defending against backdooring attacks on deep neural networks"
    - "semantic adversarial examples"
    - "generalized byzantine tolerant sgd"
    - "byzantine robust distributed learning towards optimal statistical rates"
    - "spectral signatures in backdoor attacks"
    - "how to backdoor federated learning"
    - "poison frogs targeted clean label poisoning attacks on neural networks"

Connected through hyperedge HE-5 (induced by source paper "detox a redundancy based framework for faster and more robust gradient aggregation"):
  with members:
    - "fall of empires breaking byzantine tolerant sgd by inner product manipulation"
    - "fast and secure distributed learning in high dimension"
    - "sgd decentralized byzantine resilience"
    - "byzantine stochastic gradient descent"
    - "signsgd with majority vote is communication efficient and fault tolerant"
    - "zeno byzantine suspicious stochastic gradient descent"
    - "generalized byzantine tolerant sgd"
    - "byzantine robust distributed learning towards optimal statistical rates"

Connected through hyperedge HE-6 (induced by source paper "fast machine learning with byzantine workers and servers"):
  with members:
    - "fall of empires breaking byzantine tolerant sgd by inner product manipulation"
    - "detox a redundancy based framework for faster and more robust gradient aggregation"
    - "communication efficient distributed sgd with sketching"
    - "sgd decentralized byzantine resilience"
    - "zeno byzantine suspicious stochastic gradient descent"
    - "generalized byzantine tolerant sgd"
    - "phocas dimensional byzantine resilient stochastic gradient descent"
    - "poseidon an efficient communication architecture for distributed deep learning on gpu clusters"

Connected through hyperedge HE-7 (induced by source paper "how to backdoor federated learning"):
  with members:
    - "strip a defence against trojan attacks on deep neural networks"
    - "towards federated learning at scale system design"
    - "contamination attacks and mitigation in multi party machine learning"
    - "distributed training with heterogeneous data bridging median and mean based algorithms"
    - "detecting backdoor attacks on deep neural networks by activation clustering"
    - "analyzing federated learning through an adversarial lens"
    - "fine pruning defending against backdooring attacks on deep neural networks"
    - "multi party poisoning through generalized p tampering"

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)