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 the multiple traveling salesmen problem with permutation invariant pooling networks"
  Abstract: While there are optimal TSP solvers, as well as recent learning-based approaches, the generalization of the TSP to the Multiple Traveling Salesmen Problem is much less studied. Here, we design a neural network solution that treats the salesmen, cities and depot as three different sets of varying cardinalities. We apply a novel technique that combines elements from recent architectures that were developed for sets, as well as elements from graph networks. Coupled with new constraint enforcing output layers, a dedicated loss, and a search method, our solution is shown to outperform all the meta-heuristics of the leading solver in the field.

Connected through hyperedge HE-1 (induced by source paper "an efficient graph convolutional network technique for the travelling salesman problem"):
  with members:
    - "learning heuristics over large graphs via deep reinforcement learning"
    - "graph2seq scalable learning dynamics for graphs"
    - "machine learning for combinatorial optimization a methodological tour d horizon"
    - "residual gated graph convnets"
    - "combinatorial optimization with graph convolutional networks and guided tree search"
    - "geometric deep learning going beyond euclidean data"
    - "learning combinatorial optimization algorithms over graphs"
    - "encoding sentences with graph convolutional networks for semantic role labeling"

Connected through hyperedge HE-2 (induced by source paper "learning scalable and transferable multi robot machine sequential assignment planning via graph embedding"):
  with members:
    - "review of multi agent algorithms for collective behavior a structural taxonomy"
    - "parameter space noise for exploration"
    - "learning combinatorial optimization algorithms over graphs"
    - "neural combinatorial optimization with reinforcement learning"
    - "discriminative embeddings of latent variable models for structured data"
    - "learning modular neural network policies for multi task and multi robot transfer"
    - "continuous control with deep reinforcement learning"
    - "deep reinforcement learning with double q learning"

Connected through hyperedge HE-3 (induced by source paper "neural large neighborhood search for the capacitated vehicle routing problem"):
  with members:
    - "an efficient graph convolutional network technique for the travelling salesman problem"
    - "machine learning for combinatorial optimization a methodological tour d horizon"
    - "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-4 (induced by source paper "learning improvement heuristics for solving the travelling salesman problem"):
  with members:
    - "machine learning for combinatorial optimization a methodological tour d horizon"
    - "attention is all you need"
    - "learning combinatorial optimization algorithms over graphs"
    - "time limits in reinforcement learning"
    - "neural combinatorial optimization with reinforcement learning"
    - "asynchronous methods for deep reinforcement learning"

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)