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: "fully decentralized policies for multi agent systems an information theoretic approach"
  Abstract: Learning cooperative policies for multi-agent systems is often challenged by partial observability and a lack of coordination. In some settings, the structure of a problem allows a distributed solution with limited communication. Here, we consider a scenario where no communication is available, and instead we learn local policies for all agents that collectively mimic the solution to a centralized multi-agent static optimization problem. Our main contribution is an information theoretic framework based on rate distortion theory which facilitates analysis of how well the resulting fully decentralized policies are able to reconstruct the optimal solution. Moreover, this framework provides a natural extension that addresses which nodes an agent should communicate with to improve the performance of its individual policy.

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"
    - "a little is enough circumventing defenses for distributed learning"
    - "communication compression for decentralized training"

Connected through hyperedge HE-2 (induced by source paper "context aware deep q network for decentralized cooperative reconnaissance by a robotic swarm"):
  with members:
    - "policy gradient with value function approximation for collective multiagent planning"
    - "review of multi agent algorithms for collective behavior a structural taxonomy"
    - "learning for multi robot cooperation in partially observable stochastic environments with macro actions"

Connected through hyperedge HE-3 (induced by source paper "tackling climate change with machine learning"):
  with members:
    - "pvnet a lrcn architecture for spatio temporal photovoltaic powerforecasting from numerical weather prediction"
    - "truck traffic monitoring with satellite images"
    - "artificial intelligence for social good"
    - "efficient multi objective optimization through population based parallel surrogate search"
    - "agriculture commodity arrival prediction using remote sensing data insights and beyond"
    - "decentralized flood forecasting using deep neural networks"
    - "machine vision for natural gas methane emissions detection using an infrared camera"
    - "learning optimal solutions for extremely fast ac optimal power flow"

Connected through hyperedge HE-4 (induced by source paper "cesma centralized expert supervises multi agents"):
  with members:
    - "decentralization of multiagent policies by learning what to communicate"
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "multi agent common knowledge reinforcement learning"
    - "counterfactual multi agent policy gradients"
    - "stabilising experience replay for deep multi agent reinforcement learning"
    - "emergence of grounded compositional language in multi agent populations"
    - "deep decentralized multi task multi agent reinforcement learning under partial observability"
    - "action branching architectures for deep reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "decentralization of multiagent policies by learning what to communicate"):
  with members:
    - "deep sets"
    - "counterfactual multi agent policy gradients"
    - "multi agent actor critic for mixed cooperative competitive environments"
    - "learning multiagent communication with backpropagation"
    - "learning to communicate to solve riddles with deep distributed recurrent q networks"
    - "binaryconnect training deep neural networks with binary weights during propagations"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "deep learning with limited numerical precision"

Connected through hyperedge HE-6 (induced by source paper "learning from my partner s actions roles in decentralized robot teams"):
  with members:
    - "decentralization of multiagent policies by learning what to communicate"
    - "multi agent actor critic for mixed cooperative competitive environments"
    - "cooperative inverse reinforcement learning"
    - "learning to communicate with deep multi agent reinforcement learning"
    - "learning multiagent communication with backpropagation"
    - "optimal control of two player systems with output feedback"
    - "planning for decentralized control of multiple robots under uncertainty"
    - "decentralized stochastic control with partial history sharing a common information approach"

Connected through hyperedge HE-7 (induced by source paper "modelling the dynamic joint policy of teammates with attention multi agent ddpg"):
  with members:
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "modeling others using oneself in multi agent reinforcement learning"
    - "autonomous agents modelling other agents a comprehensive survey and open problems"
    - "mean field multi agent reinforcement learning"
    - "a deep policy inference q network for multi agent systems"
    - "counterfactual multi agent policy gradients"
    - "learning with opponent learning awareness"
    - "parameter sharing deep deterministic policy gradient for cooperative multi agent reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "communication compression for decentralized training"):
  with members:
    - "don t use large mini batches use local sgd"
    - "sparsified sgd with memory"
    - "training dnns with hybrid block floating point"
    - "gradient sparsification for communication efficient distributed optimization"
    - "dynamic safe interruptibility for decentralized multi agent reinforcement learning"
    - "deep decentralized multi task multi agent reinforcement learning under partial observability"
    - "randomized distributed mean estimation accuracy vs communication"
    - "qsgd communication efficient sgd via gradient quantization and encoding"

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)