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: "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.

Connected through hyperedge HE-1 (induced by source paper "graph neural networks for maximum constraint satisfaction"):
  with members:
    - "learning to reason leveraging neural networks for approximate dnf counting"
    - "learning a sat solver from single bit supervision"
    - "combinatorial optimization with graph convolutional networks and guided tree search"
    - "scalable kernelization for maximum independent sets"
    - "learning combinatorial optimization algorithms over graphs"
    - "finding near optimal independent sets at scale"
    - "a simple model to generate hard satisfiable instances"
    - "many hard examples in exact phase transitions with application to generating hard satisfiable instances"

Connected through hyperedge HE-2 (induced by source paper "run csp unsupervised learning of message passing networks for binary constraint satisfaction problems"):
  with members:
    - "coloring big graphs with alphagozero"
    - "learning to reason leveraging neural networks for approximate dnf counting"
    - "learning a sat solver from single bit supervision"
    - "learning to solve np complete problems a graph neural network for decision tsp"
    - "combinatorial optimization with graph convolutional networks and guided tree search"
    - "learning combinatorial optimization algorithms over graphs"
    - "neural combinatorial optimization with reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "janos an integrated predictive and prescriptive modeling framework"):
  with members:
    - "machine learning for combinatorial optimization a methodological tour d horizon"
    - "principled deep neural network training through linear programming"
    - "improving optimization bounds using machine learning decision diagrams meet deep reinforcement learning"
    - "deep neural networks as 0 1 mixed integer linear programs a feasibility study"
    - "bounding and counting linear regions of deep neural networks"
    - "julia a fresh approach to numerical computing"

Connected through hyperedge HE-4 (induced by source paper "hypergcn a new method of training graph convolutional networks on hypergraphs"):
  with members:
    - "exact k recommendation via maximal clique optimization"
    - "confidence based graph convolutional networks for semi supervised learning"
    - "multi label image recognition with graph convolutional networks"
    - "approximate k cover in hypergraphs efficient algorithms and applications"
    - "planted hitting set recovery in hypergraphs"
    - "a comprehensive survey on graph neural networks"
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "generalizing the hypergraph laplacian via a diffusion process with mediators"

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)