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 to solve np complete problems a graph neural network for decision tsp"
  Abstract: Graph Neural Networks (GNN) are a promising technique for bridging differential programming and combinatorial domains. GNNs employ trainable modules which can be assembled in different configurations that reflect the relational structure of each problem instance. In this paper, we show that GNNs can learn to solve, with very little supervision, the decision variant of the Traveling Salesperson Problem (TSP), a highly relevant $\mathcal{NP}$-Complete problem. Our model is trained to function as an effective message-passing algorithm in which edges (embedded with their weights) communicate with vertices for a number of iterations after which the model is asked to decide whether a route with cost $<C$ exists. We show that such a network can be trained with sets of dual examples: given the optimal tour cost $C^{*}$, we produce one decision instance with target cost $x\%$ smaller and one with target cost $x\%$ larger than $C^{*}$. We were able to obtain $80\%$ accuracy training with $-2\%,+2\%$ deviations, and the same trained model can generalize for more relaxed deviations with increasing performance. We also show that the model is capable of generalizing for larger problem sizes. Finally, we provide a method for predicting the optimal route cost within $2\%$ deviation from the ground truth. In summary, our work shows that Graph Neural Networks are powerful enough to solve $\mathcal{NP}$-Complete problems which combine symbolic and numeric data.

Connected through hyperedge HE-1 (induced by source paper "graph neural networks meet neural symbolic computing a survey and perspective"):
  with members:
    - "the next decade in ai four steps towards robust artificial intelligence"
    - "run csp unsupervised learning of message passing networks for binary constraint satisfaction problems"
    - "recurrent independent mechanisms"
    - "deep learning for symbolic mathematics"
    - "the neuro symbolic concept learner interpreting scenes words and sentences from natural supervision"
    - "neural symbolic computing an effective methodology for principled integration of machine learning and reasoning"
    - "the use of deep learning for symbolic integration a review of lample and charton 2019"
    - "learning to reason leveraging neural networks for approximate dnf counting"

Connected through hyperedge HE-2 (induced by source paper "it s not what machines can learn it s what we cannot teach"):
  with members:
    - "mipaal mixed integer program as a layer"
    - "generating difficult sat instances by preventing triangles"
    - "satnet bridging deep learning and logical reasoning using a differentiable satisfiability solver"
    - "hidden stratification causes clinically meaningful failures in machine learning for medical imaging"
    - "guiding high performance sat solvers with unsat core predictions"
    - "a survey on bias and fairness in machine learning"
    - "deep learning for symbolic mathematics"
    - "the use of deep learning for symbolic integration a review of lample and charton 2019"

Connected through hyperedge HE-3 (induced by source paper "a deep reinforcement learning algorithm using dynamic attention model for vehicle routing problems"):
  with members:
    - "learning heuristics over large graphs via deep reinforcement learning"
    - "learning a sat solver from single bit supervision"
    - "machine learning for combinatorial optimization a methodological tour d horizon"
    - "combinatorial optimization with graph convolutional networks and guided tree search"
    - "attention is all you need"
    - "neural message passing for quantum chemistry"
    - "learning combinatorial optimization algorithms over graphs"
    - "neural combinatorial optimization with reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "nlocalsat boosting local search with solution prediction"):
  with members:
    - "guiding high performance sat solvers with unsat core predictions"
    - "learning a sat solver from single bit supervision"
    - "combinatorial optimization with graph convolutional networks and guided tree search"
    - "graph based convolutional neural network"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-5 (induced by source paper "pdp a general neural framework for learning constraint satisfaction solvers"):
  with members:
    - "a comprehensive survey on graph neural networks"
    - "learning a sat solver from single bit supervision"
    - "machine learning for combinatorial optimization a methodological tour d horizon"
    - "combinatorial optimization with graph convolutional networks and guided tree search"
    - "deep sets"
    - "streamlining variational inference for constraint satisfaction problems"
    - "inference in probabilistic graphical models by graph neural networks"
    - "recurrent relational networks"

Connected through hyperedge HE-6 (induced by source paper "typed graph networks"):
  with members:
    - "learning a sat solver from single bit supervision"
    - "machine learning for combinatorial optimization a methodological tour d horizon"
    - "recurrent relational networks for complex relational reasoning"
    - "multitask learning on graph neural networks learning multiple graph centrality measures with a unified network"
    - "relational inductive biases deep learning and graph networks"
    - "progressive growing of gans for improved quality stability and variation"
    - "recurrent relational networks"
    - "a simple neural network module for relational reasoning"

Connected through hyperedge HE-7 (induced by source paper "learning to reason leveraging neural networks for approximate dnf counting"):
  with members:
    - "learning a sat solver from single bit supervision"
    - "weisfeiler and leman go neural higher order graph neural networks"
    - "approximate knowledge compilation by online collapsed importance sampling"
    - "how powerful are graph neural networks"
    - "inference in probabilistic graphical models by graph neural networks"
    - "on hashing based approaches to approximate dnf counting"
    - "gated graph sequence neural networks"
    - "fast and accurate deep network learning by exponential linear units elus"

Connected through hyperedge HE-8 (induced by source paper "graph colouring meets deep learning effective graph neural network models for combinatorial problems"):
  with members:
    - "learning a sat solver from single bit supervision"
    - "machine learning for combinatorial optimization a methodological tour d horizon"
    - "recurrent relational networks for complex relational reasoning"
    - "relational inductive biases deep learning and graph networks"
    - "recurrent relational networks"
    - "neural message passing for quantum chemistry"
    - "combining the k cnf and xor phase transitions"
    - "very deep convolutional networks for large scale image recognition"

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)