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: "meta modeling game for deriving theory consistent microstructure based traction separation laws via deep reinforcement learning"
  Abstract: Abstract   This paper presents a new meta-modeling framework that employs deep reinforcement learning (DRL) to generate mechanical constitutive models for interfaces. The constitutive models are conceptualized as information flow in directed graphs. The process of writing constitutive models is simplified as a sequence of forming graph edges with the goal of maximizing the model score (a function of accuracy, robustness and forward prediction quality). Thus meta-modeling can be formulated as a Markov decision process with well-defined states, actions, rules, objective functions and rewards. By using neural networks to estimate policies and state values, the computer agent is able to efficiently self-improve the constitutive model it generated through self-playing, in the same way AlphaGo Zero (the algorithm that outplayed the world champion in the game of Go) improves its gameplay. Our numerical examples show that this automated meta-modeling framework does not only produces models which outperform existing cohesive models on benchmark traction–separation data, but is also capable of detecting hidden mechanisms among micro-structural features and incorporating them in constitutive models to improve the forward prediction accuracy, both of which are difficult tasks to do manually.

Connected through hyperedge HE-1 (induced by source paper "geometric deep learning for computational mechanics part i anisotropic hyperelasticity"):
  with members:
    - "a physics constrained data driven approach based on locally convex reconstruction for noisy database"
    - "a comprehensive survey on graph neural networks"
    - "a deep material network for multiscale topology learning and accelerated nonlinear modeling of heterogeneous materials"
    - "neural network with unbounded activation functions is universal approximator"
    - "dynamic edge conditioned filters in convolutional neural networks on graphs"
    - "graph2vec learning distributed representations of graphs"
    - "node2vec scalable feature learning for networks"
    - "semi supervised classification with graph convolutional networks"

Connected through hyperedge HE-2 (induced by source paper "physics informed multi lstm networks for metamodeling of nonlinear structures"):
  with members:
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "a phase field model for cohesive fracture in micropolar continua"):
  with members:
    - "dolfin automated finite element computing"

Connected through hyperedge HE-4 (induced by source paper "machine learning materials physics multi resolution neural networks learn the free energy and nonlinear elastic response of evolving microstructures"):
  with members:
    - "petiga a framework for high performance isogeometric analysis"

Connected through hyperedge HE-5 (induced by source paper "a cooperative game for automated learning of elasto plasticity knowledge graphs and models with ai guided experimentation"):
  with members:
    - "modeling others using oneself in multi agent reinforcement learning"
    - "relational inductive biases deep learning and graph networks"
    - "stochastic training of residual networks a differential equation viewpoint"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "building machines that learn and think like people"
    - "learning to communicate with deep multi agent reinforcement learning"
    - "multiagent cooperation and competition with deep reinforcement learning"
    - "bayesian reinforcement learning a survey"

Connected through hyperedge HE-6 (induced by source paper "general solutions for nonlinear differential equations a rule based self learning approach using deep reinforcement learning"):
  with members:
    - "a cooperative game for automated learning of elasto plasticity knowledge graphs and models with ai guided experimentation"
    - "hidden fluid mechanics a navier stokes informed deep learning framework for assimilating flow visualization data"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "learning to reinforcement learn"
    - "adam a method for stochastic optimization"
    - "playing atari with deep reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "sai a sensible artificial intelligence that plays go"):
  with members:
    - "alphaseq sequence discovery with deep reinforcement learning"
    - "reinforcement evolutionary learning method for self learning"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "multi labelled value networks for computer go"

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)