human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "graph colouring meets deep learning effective graph neural network models for combinatorial problems"):
  Abstract: Deep learning has consistently defied state-of-the-art techniques in many fields over the last decade. However, we are just beginning to understand the capabilities of neural learning in symbolic domains. Deep learning architectures that employ parameter sharing over graphs can produce models which can be trained on complex properties of relational data. These include highly relevant NP-Complete problems, such as SAT and TSP. In this work, we showcase how Graph Neural Networks (GNN) can be engineered -- with a very simple architecture -- to solve the fundamental combinatorial problem of graph colouring. Our results show that the model, which achieves high accuracy upon training on random instances, is able to generalise to graph distributions different from those seen at training time. Further, it performs better than the Neurosat, Tabucol and greedy baselines for some distributions. In addition, we show how vertex embeddings can be clustered in multidimensional spaces to yield constructive solutions even though our model is only trained as a binary classifier. In summary, our results contribute to shorten the gap in our understanding of the algorithms learned by GNNs, as well as hoarding empirical evidence for their capability on hard combinatorial problems. Our results thus contribute to the standing challenge of integrating robust learning and symbolic reasoning in Deep Learning systems.

Includes members:
  Member "learning a sat solver from single bit supervision":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "teaching temporal logics to neural networks")
  Member "machine learning for combinatorial optimization a methodological tour d horizon":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "lossless compression of deep neural networks")
  Member "learning to solve np complete problems a graph neural network for decision tsp":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "graph neural networks meet neural symbolic computing a survey and perspective")
  Member "recurrent relational networks for complex relational reasoning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "relational action forecasting")
  Member "relational inductive biases deep learning and graph networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "intelligence physics and information the tradeoff between accuracy and simplicity in machine learning")
  Member "recurrent relational networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "disentangling physical dynamics from unknown factors for unsupervised video prediction")
  Member "neural message passing for quantum chemistry":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "intelligence physics and information the tradeoff between accuracy and simplicity in machine learning")
  Member "combining the k cnf and xor phase transitions":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "quantitative verification of neural networks and its security applications")

Question: Please tell me which class the source paper 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)