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: "distributed optimization under adversarial nodes"
  Abstract: We investigate the vulnerabilities of consensus-based distributed optimization protocols to nodes that deviate from the prescribed update rule (e.g., due to failures or adversarial attacks). We first characterize certain fundamental limitations on the performance of any distributed optimization algorithm in the presence of adversaries. We then propose a resilient distributed optimization algorithm that guarantees that the non-adversarial nodes converge to the convex hull of the minimizers of their local functions under certain conditions on the graph topology, regardless of the actions of a certain number of adversarial nodes. In particular, we provide sufficient conditions on the graph topology to tolerate a bounded number of adversaries in the neighborhood of every non-adversarial node, and necessary and sufficient conditions to tolerate a globally bounded number of adversaries. For situations where there are up to F adversaries in the neighborhood of every node, we use the concept of maximal F-local sets of graphs to provide lower bounds on the distance-to-optimality of achievable solutions under any algorithm. We show that finding the size of such sets is NP-hard.

Connected through hyperedge HE-1 (induced by source paper "resilient cyberphysical systems and their application drivers a technology roadmap"):
  with members:
    - "traditional and heavy tailed self regularization in neural network models"
    - "numerical verification of affine systems with up to a billion dimensions"
    - "juliareach a toolbox for set based reachability"
    - "heavy tailed universality predicts trends in test accuracies for very large pre trained deep neural networks"
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "formal security analysis of neural networks using symbolic intervals"
    - "a dual approach to scalable verification of deep networks"
    - "verisig verifying safety properties of hybrid systems with neural network controllers"

Connected through hyperedge HE-2 (induced by source paper "optimization for reinforcement learning from single agent to cooperative agents"):
  with members:
    - "finite time error bounds for linear stochastic approximation and td learning"
    - "value propagation for decentralized networked deep multi agent reinforcement learning"
    - "a finite time analysis of temporal difference learning with linear function approximation"
    - "finite sample analyses for fully decentralized multi agent reinforcement learning"
    - "deep reinforcement learning for multi agent systems a review of challenges solutions and applications"
    - "addressing function approximation error in actor critic methods"
    - "finite sample analysis for decentralized batch multi agent reinforcement learning with networked agents"
    - "multi agent reinforcement learning via double averaging primal dual optimization"

Connected through hyperedge HE-3 (induced by source paper "determining r and r s robustness of digraphs using mixed integer linear programming"):
  with members:
    - "determining r robustness of arbitrary digraphs using zero one linear integer programming"
    - "secure distributed state estimation of an lti system over time varying networks and analog erasure channels"
    - "resilient consensus of second order agent networks asynchronous update rules with delays"
    - "on connectivity and robustness in random intersection graphs"
    - "resilient randomized quantized consensus"
    - "r robustness and r s robustness of circulant graphs"
    - "robustness and algebraic connectivity of random interdependent networks"
    - "asynchronous convex consensus in the presence of crash faults"

Connected through hyperedge HE-4 (induced by source paper "byzantine fault tolerant distributed linear regression"):
  with members:
    - "secure state estimation with byzantine sensors a probabilistic approach"
    - "byzantine stochastic gradient descent"
    - "signsgd with majority vote is communication efficient and fault tolerant"
    - "byzantine resilient distributed observers for lti systems"
    - "generalized byzantine tolerant sgd"
    - "byzantine robust distributed learning towards optimal statistical rates"
    - "sever a robust meta algorithm for stochastic optimization"
    - "finite time guarantees for byzantine resilient distributed state estimation with noisy measurements"

Connected through hyperedge HE-5 (induced by source paper "secure distributed on device learning networks with byzantine adversaries"):
  with members:
    - "supervised learning under distributed features"
    - "byzantine stochastic gradient descent"
    - "rsa byzantine robust stochastic aggregation methods for distributed learning from heterogeneous datasets"
    - "byzantine robust distributed learning towards optimal statistical rates"
    - "distributed statistical machine learning in adversarial settings byzantine gradient descent"
    - "proximity without consensus in online multiagent optimization"
    - "federated multi task learning"
    - "federated optimization distributed optimization beyond the datacenter"

Connected through hyperedge HE-6 (induced by source paper "a new approach for distributed hypothesis testing with extensions to byzantine resilience"):
  with members:
    - "byzantine resilient distributed observers for lti systems"
    - "distributed observers for lti systems"
    - "defending non bayesian learning against adversarial attacks"
    - "exponentially fast parameter estimation in networks using distributed dual averaging"
    - "matrix representation of iterative approximate byzantine consensus in directed graphs"
    - "iterative approximate byzantine consensus in arbitrary directed graphs"

Connected through hyperedge HE-7 (induced by source paper "resilient consensus through event based communication"):
  with members:
    - "resilient consensus of second order agent networks asynchronous update rules with delays"
    - "on connectivity and robustness in random intersection graphs"
    - "resilient randomized quantized consensus"
    - "a jamming resilient algorithm for self triggered network coordination"
    - "fault and byzantine tolerant self stabilizing mobile robots gathering feasibility study"
    - "iterative approximate byzantine consensus in arbitrary directed graphs"

Connected through hyperedge HE-8 (induced by source paper "a risk security tradeoff in graphical coordination games"):
  with members:
    - "spread of information with confirmation bias in cyber social networks"
    - "security against impersonation attacks in distributed systems"
    - "metastability of logit dynamics for coordination games"

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.SY(Systems and Control)