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: "exact combinatorial optimization with graph convolutional neural networks"
  Abstract: Combinatorial optimization problems are typically tackled by the branch-and-bound paradigm. We propose a new graph convolutional neural network model for learning branch-and-bound variable selection policies, which leverages the natural variable-constraint bipartite graph representation of mixed-integer linear programs. We train our model via imitation learning from the strong branching expert rule, and demonstrate on a series of hard problems that our approach produces policies that improve upon state-of-the-art machine-learning methods for branching and generalize to instances significantly larger than seen during training. Moreover, we improve for the first time over expert-designed branching rules implemented in a state-of-the-art solver on large problems. Code for reproducing all the experiments can be found at this https URL.

Connected through hyperedge HE-1 (induced by source paper "a survey on the expressive power of graph neural networks"):
  with members:
    - "random features strengthen graph neural networks"
    - "generalization and representational limits of graph neural networks"
    - "universal invariant and equivariant graph neural networks"
    - "exact k recommendation via maximal clique optimization"
    - "constant time graph neural networks"
    - "kgat knowledge graph attention network for recommendation"
    - "understanding attention and generalization in graph neural networks"
    - "on the universality of invariant networks"

Connected through hyperedge HE-2 (induced by source paper "parameterizing branch and bound search trees to learn branching policies"):
  with members:
    - "learning action representations for reinforcement learning"
    - "machine learning for combinatorial optimization a methodological tour d horizon"
    - "exgate externally controlled gating for feature based attention in artificial neural networks"
    - "beyond skip connections top down modulation for object detection"
    - "feature pyramid networks for object detection"
    - "deep reinforcement learning in large discrete action spaces"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "graph neural ordinary differential equations"):
  with members:
    - "universal differential equations for scientific machine learning"
    - "latent odes for irregularly sampled time series"
    - "graph neural networks exponentially lose expressive power for node classification"
    - "anode unconditionally accurate memory efficient gradients for neural odes"
    - "hamiltonian graph networks with ode integrators"
    - "gresnet graph residual network for reviving deep gnns from suspended animation"
    - "fast and deep graph neural networks"
    - "a differentiable programming system to bridge machine learning and scientific computing"

Connected through hyperedge HE-4 (induced by source paper "differentiation of blackbox combinatorial solvers"):
  with members:
    - "mipaal mixed integer program as a layer"
    - "satnet bridging deep learning and logical reasoning using a differentiable satisfiability solver"
    - "guiding high performance sat solvers with unsat core predictions"
    - "raynet learning volumetric 3d reconstruction with ray potentials"
    - "smart predict and optimize for hard combinatorial optimization problems"
    - "end to end learning and optimization on graphs"
    - "learning a sat solver from single bit supervision"
    - "direct optimization through arg max for discrete variational auto encoder"

Connected through hyperedge HE-5 (induced by source paper "neural network branching for neural network verification"):
  with members:
    - "optimization and abstraction a synergistic approach for analyzing neural network robustness"
    - "fast neural network verification via shadow prices"
    - "branch and bound for piecewise linear neural network verification"
    - "efficient formal safety analysis of neural networks"
    - "evaluating robustness of neural networks with mixed integer programming"
    - "learning combinatorial optimization algorithms over graphs"
    - "reluplex an efficient smt solver for verifying deep neural networks"
    - "neural combinatorial optimization with reinforcement learning"

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:
    - "an efficient graph convolutional network technique for the travelling salesman problem"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "learning chordal extensions"):
  with members:
    - "machine learning for combinatorial optimization a methodological tour d horizon"
    - "combinatorial optimization with graph convolutional networks and guided tree search"
    - "agile autonomous driving using end to end deep imitation learning"
    - "representation learning on graphs methods and applications"
    - "on the minimum chordal completion polytope"
    - "semi supervised classification with graph convolutional networks"
    - "convolutional networks on graphs for learning molecular fingerprints"
    - "a reduction of imitation learning and structured prediction to no regret online learning"

Connected through hyperedge HE-8 (induced by source paper "optimal solution predictions for mixed integer programs"):
  with members:
    - "reinforcement learning for integer programming learning to cut"
    - "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"
    - "learning combinatorial optimization algorithms over graphs"
    - "xgboost a scalable tree boosting system"

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)