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: "an efficient graph convolutional network technique for the travelling salesman problem"
  Abstract: This paper introduces a new learning-based approach for approximately solving the Travelling Salesman Problem on 2D Euclidean graphs. We use deep Graph Convolutional Networks to build efficient TSP graph representations and output tours in a non-autoregressive manner via highly parallelized beam search. Our approach outperforms all recently proposed autoregressive deep learning techniques in terms of solution quality, inference speed and sample efficiency for problem instances of fixed graph sizes. In particular, we reduce the average optimality gap from 0.52% to 0.01% for 50 nodes, and from 2.26% to 1.39% for 100 nodes. Finally, despite improving upon other learning-based approaches for TSP, our approach falls short of standard Operations Research solvers.

Connected through hyperedge HE-1 (induced by source paper "benchmarking graph neural networks"):
  with members:
    - "understanding attention and generalization in graph neural networks"
    - "a fair comparison of graph neural networks for graph classification"
    - "are powerful graph neural nets necessary a dissection on graph classification"
    - "a two step graph convolutional decoder for molecule generation"
    - "on graph classification networks datasets and baselines"
    - "self attention graph pooling"
    - "gnn film graph neural networks with feature wise linear modulation"
    - "a gentle introduction to deep learning for graphs"

Connected through hyperedge HE-2 (induced by source paper "what graph neural networks cannot learn depth vs width"):
  with members:
    - "edgenets edge varying graph neural networks"
    - "universal invariant and equivariant graph neural networks"
    - "lanczosnet multi scale deep graph convolutional networks"
    - "mincut pooling in graph neural networks"
    - "break the ceiling stronger multi scale deep graph convolutional networks"
    - "understanding the representation power of graph neural networks in learning graph topology"
    - "on the universality of invariant networks"
    - "approximation ratios of graph neural networks for combinatorial problems"

Connected through hyperedge HE-3 (induced by source paper "combinatorial optimization by graph pointer networks and hierarchical reinforcement learning"):
  with members:
    - "latent space policies for hierarchical reinforcement learning"
    - "combinatorial optimization with graph convolutional networks and guided tree search"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "how powerful are graph neural networks"
    - "learning combinatorial optimization algorithms over graphs"
    - "neural combinatorial optimization with reinforcement learning"
    - "asynchronous methods for deep reinforcement learning"
    - "hierarchical deep reinforcement learning integrating temporal abstraction and intrinsic motivation"

Connected through hyperedge HE-4 (induced by source paper "neural large neighborhood search for the capacitated vehicle routing problem"):
  with members:
    - "machine learning for combinatorial optimization a methodological tour d horizon"
    - "learning the multiple traveling salesmen problem with permutation invariant pooling networks"
    - "residual gated graph convnets"
    - "deep learning assisted heuristic tree search for the container pre marshalling problem"
    - "learning combinatorial optimization algorithms over graphs"
    - "neural combinatorial optimization with reinforcement learning"
    - "adam a method for stochastic optimization"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-5 (induced by source paper "on learning paradigms for the travelling salesman problem"):
  with members:
    - "machine learning for combinatorial optimization a methodological tour d horizon"
    - "learning combinatorial optimization algorithms over graphs"
    - "neural combinatorial optimization with reinforcement learning"
    - "geometric deep learning on graphs and manifolds using mixture model cnns"
    - "semi supervised classification with graph convolutional networks"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "learning multiagent communication with backpropagation"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "how to evaluate machine learning approaches for combinatorial optimization application to the travelling salesman problem"):
  with members:
    - "exact combinatorial optimization with graph convolutional neural networks"
    - "residual gated graph convnets"
    - "improving optimization bounds using machine learning decision diagrams meet deep reinforcement learning"
    - "attention is all you need"
    - "learning combinatorial optimization algorithms over graphs"
    - "neural combinatorial optimization with reinforcement learning"
    - "asynchronous methods for deep reinforcement learning"
    - "discriminative embeddings of latent variable models for structured data"

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)