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: "learning permutations with sinkhorn policy gradient"
  Abstract: Many problems at the intersection of combinatorics and computer science require solving for a permutation that optimally matches, ranks, or sorts some data. These problems usually have a task-specific, often non-differentiable objective function that data-driven algorithms can use as a learning signal. In this paper, we propose the Sinkhorn Policy Gradient (SPG) algorithm for learning policies on permutation matrices. The actor-critic neural network architecture we introduce for SPG uniquely decouples representation learning of the state space from the highly-structured action space of permutations with a temperature-controlled Sinkhorn layer. The Sinkhorn layer produces continuous relaxations of permutation matrices so that the actor-critic architecture can be trained end-to-end. Our empirical results show that agents trained with SPG can perform competitively on sorting, the Euclidean TSP, and matching tasks. We also observe that SPG is significantly more data efficient at the matching task than the baseline methods, which indicates that SPG is conducive to learning representations that are useful for reasoning about permutations.

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"
    - "exploratory combinatorial optimization with reinforcement 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"

Connected through hyperedge HE-2 (induced by source paper "wasserstein based graph alignment"):
  with members:
    - "learning graphs from data a signal representation perspective"
    - "on the convergence of adam and beyond"
    - "netlsd hearing the shape of a graph"
    - "ds a flexible scalable and provably tight relaxation for matching problems"
    - "improved bilinear pooling with cnns"
    - "robust principal component analysis on graphs"
    - "learning laplacian matrix in smooth graph signal representations"

Connected through hyperedge HE-3 (induced by source paper "learning index selection with structured action spaces"):
  with members:
    - "learning action representations for reinforcement learning"
    - "simple random search provides a competitive approach to reinforcement learning"
    - "the case for automatic database administration using deep reinforcement learning"
    - "darts differentiable architecture search"
    - "lift reinforcement learning in computer systems by learning from demonstrations"
    - "towards a hands free query optimizer through deep learning"
    - "rainbow combining improvements in deep reinforcement learning"
    - "deep reinforcement learning a brief survey"

Connected through hyperedge HE-4 (induced by source paper "learning combinatorial embedding networks for deep graph matching"):
  with members:
    - "sinkhorn autoencoders"
    - "coloring big graphs with alphagozero"
    - "graph neural networks a review of methods and applications"
    - "deep sets"
    - "node2vec scalable feature learning for networks"
    - "semi supervised classification with graph convolutional networks"
    - "line large scale information network embedding"
    - "deepwalk online learning of social representations"

Connected through hyperedge HE-5 (induced by source paper "got an optimal transport framework for graph comparison"):
  with members:
    - "learning graphs from data a signal representation perspective"
    - "on the convergence of adam and beyond"
    - "gromov wasserstein alignment of word embedding spaces"
    - "netlsd hearing the shape of a graph"
    - "implicit reparameterization gradients"
    - "ds a flexible scalable and provably tight relaxation for matching problems"
    - "improved bilinear pooling with cnns"
    - "network topology inference from spectral templates"

Connected through hyperedge HE-6 (induced by source paper "tauriel targeting traveling salesman problem with a deep reinforcement learning inspired architecture"):
  with members:
    - "machine learning for combinatorial optimization a methodological tour d horizon"
    - "attention is all you need"
    - "learning combinatorial optimization algorithms over graphs"
    - "neural combinatorial optimization with reinforcement learning"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "continuous control with deep reinforcement learning"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-7 (induced by source paper "machine learning for combinatorial optimization a methodological tour d horizon"):
  with members:
    - "exact combinatorial optimization with graph convolutional neural networks"
    - "predicting tactical solutions to operational planning problems under imperfect information"
    - "learning to optimize neural nets"
    - "automated treatment planning in radiation therapy using generative adversarial networks"
    - "learning a sat solver from single bit supervision"
    - "deterministic implementations for reproducibility in deep reinforcement learning"
    - "generative adversarial networks an overview"
    - "predicting solution summaries to integer linear programs under imperfect information with machine learning"

Connected through hyperedge HE-8 (induced by source paper "seq2slate re ranking and slate optimization with rnns"):
  with members:
    - "learning groupwise scoring functions using deep neural networks"
    - "universal transformers"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "learning a deep listwise context model for ranking refinement"
    - "top k off policy correction for a reinforce recommender system"
    - "deeprank a new deep architecture for relevance ranking in information retrieval"
    - "attention is all you need"
    - "an attention based deep net for learning to rank"

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)