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 human mixed strategy approach to deep reinforcement learning"
  Abstract: In 2015, Google's Deepmind announced an advancement in creating an autonomous agent based on deep reinforcement learning (DRL) that could beat a professional player in a series of 49 Atari games. However, the current manifestation of DRL is still immature, and has significant drawbacks. One of DRL's imperfections is its lack of "exploration" during the training process, especially when working with high-dimensional problems. In this paper, we propose a mixed strategy approach that mimics behaviors of human when interacting with environment, and create a "thinking" agent that allows for more efficient exploration in the DRL training process. The simulation results based on the Breakout game show that our scheme achieves a higher probability of obtaining a maximum score than does the baseline DRL algorithm, i.e., the asynchronous advantage actor-critic method. The proposed scheme therefore can be applied effectively to solving a complicated task in a real-world application.

Connected through hyperedge HE-1 (induced by source paper "deep reinforcement learning for cyber security"):
  with members:
    - "deep reinforcement learning for detecting malicious websites"
    - "manipulating soft tissues by deep reinforcement learning for autonomous robotic surgery"
    - "multi agent deep reinforcement learning with human strategies"
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "a new tensioning method using deep reinforcement learning for surgical pattern cutting"
    - "relational deep reinforcement learning"
    - "variance reduction for policy gradient with action dependent factorized baselines"
    - "robust deep reinforcement learning for security and safety in autonomous vehicle systems"

Connected through hyperedge HE-2 (induced by source paper "multi agent deep reinforcement learning with human strategies"):
  with members:
    - "a multi objective deep reinforcement learning framework"
    - "hierarchical deep reinforcement learning integrating temporal abstraction and intrinsic motivation"
    - "learning to communicate with deep multi agent reinforcement learning"
    - "learning multiagent communication with backpropagation"
    - "prioritized experience replay"
    - "dueling network architectures for deep reinforcement learning"
    - "learning deep features for discriminative localization"
    - "going deeper with convolutions"

Connected through hyperedge HE-3 (induced by source paper "manipulating soft tissues by deep reinforcement learning for autonomous robotic surgery"):
  with members:
    - "multi agent deep reinforcement learning with human strategies"
    - "a new tensioning method using deep reinforcement learning for surgical pattern cutting"
    - "deep reinforcement learning for multi agent systems a review of challenges solutions and applications"
    - "a multi objective deep reinforcement learning framework"
    - "iotsan fortifying the safety of iot systems"
    - "benchmarking deep reinforcement learning for continuous control"
    - "trust region policy optimization"

Connected through hyperedge HE-4 (induced by source paper "a new tensioning method using deep reinforcement learning for surgical pattern cutting"):
  with members:
    - "multi agent deep reinforcement learning with human strategies"
    - "benchmarking deep reinforcement learning for continuous control"

Connected through hyperedge HE-5 (induced by source paper "deep reinforcement learning for multi agent systems a review of challenges solutions and applications"):
  with members:
    - "a survey and critique of multiagent deep reinforcement learning"
    - "multi agent deep reinforcement learning with human strategies"
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "efficient model based deep reinforcement learning with variational state tabulation"
    - "bayesian action decoder for deep multi agent reinforcement learning"
    - "multi agent deep reinforcement learning with extremely noisy observations"
    - "maintaining cooperation in complex social dilemmas using deep reinforcement learning"
    - "a multi objective deep reinforcement learning framework"

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)