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: "exploratory combinatorial optimization with reinforcement learning"
  Abstract: Many real-world problems can be reduced to combinatorial optimization on a graph, where the subset or ordering of vertices that maximize some objective function must be found. With such tasks often NP-hard and analytically intractable, reinforcement learning (RL) has shown promise as a framework with which efficient heuristic methods to tackle these problems can be learned. Previous works construct the solution subset incrementally, adding one element at a time, however, the irreversible nature of this approach prevents the agent from revising its earlier decisions, which may be necessary given the complexity of the optimization task. We instead propose that the agent should seek to continuously improve the solution by learning to explore at test time. Our approach of exploratory combinatorial optimization (ECO-DQN) is, in principle, applicable to any combinatorial problem that can be defined on a graph. Experimentally, we show our method to produce state-of-the-art RL performance on the Maximum Cut problem. Moreover, because ECO-DQN can start from any arbitrary configuration, it can be combined with other search methods to further improve performance, which we demonstrate using a simple random search.

Connected through hyperedge HE-1 (induced by source paper "reinforcement learning for combinatorial optimization a survey"):
  with members:
    - "fast detection of maximum common subgraph via deep q learning"
    - "combinatorial optimization by graph pointer networks and hierarchical reinforcement learning"
    - "co training for policy learning"
    - "machine learning for combinatorial optimization a methodological tour d horizon"
    - "learning to perform local rewriting for combinatorial optimization"
    - "latent space policies for hierarchical reinforcement learning"
    - "learning representations in model free hierarchical reinforcement learning"
    - "learning permutations with sinkhorn policy gradient"

Connected through hyperedge HE-2 (induced by source paper "reinforcement learning enhanced quantum inspired algorithm for combinatorial optimization"):
  with members:
    - "solving np hard problems on graphs by reinforcement learning without domain knowledge"
    - "learning heuristics over large graphs via deep reinforcement learning"
    - "learning to optimize variational quantum circuits to solve combinatorial problems"
    - "ranked reward enabling self play reinforcement learning for combinatorial optimization"
    - "learning to perform local rewriting for combinatorial optimization"
    - "combinatorial optimization with graph convolutional networks and guided tree search"
    - "device placement optimization with reinforcement learning"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"

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)