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: "pattern decomposition with complex combinatorial constraints application to materials discovery"
  Abstract: Identifying important components or factors in large amounts of noisy data is a key problem in machine learning and data mining. Motivated by a pattern decomposition problem in materials discovery, aimed at discovering new materials for renewable energy, e.g. for fuel and solar cells, we introduce CombiFD, a framework for factor based pattern decomposition that allows the incorporation of a-priori knowledge as constraints, including complex combinatorial constraints. In addition, we propose a new pattern decomposition algorithm, called AMIQO, based on solving a sequence of (mixed-integer) quadratic programs. Our approach considerably outperforms the state of the art on the materials discovery problem, scaling to larger datasets and recovering more precise and physically meaningful decompositions. We also show the effectiveness of our approach for enforcing background knowledge on other application domains.

Connected through hyperedge HE-1 (induced by source paper "deep reasoning networks thinking fast and slow"):
  with members:
    - "pdp a general neural framework for learning constraint satisfaction solvers"
    - "a pca like autoencoder"
    - "melding the data decisions pipeline decision focused learning for combinatorial optimization"
    - "deep generative models with learnable knowledge constraints"
    - "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"
    - "combinatorial optimization with graph convolutional networks and guided tree search"

Connected through hyperedge HE-2 (induced by source paper "adversarial constraint learning for structured prediction"):
  with members:
    - "improved training of wasserstein gans"
    - "a semantic loss function for deep learning with symbolic knowledge"
    - "infogail interpretable imitation learning from visual demonstrations"
    - "max margin deep generative models for semi supervised learning"
    - "improved techniques for training gans"
    - "label free supervision of neural networks with physics and domain knowledge"
    - "weakly and semi supervised learning of a dcnn for semantic image segmentation"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "end to end refinement guided by pre trained prototypical classifier"):
  with members:
    - "prototypical networks for few shot learning"
    - "adversarial discriminative domain adaptation"
    - "generate to adapt aligning domains using generative adversarial networks"
    - "densely connected convolutional networks"
    - "adversarial examples in the physical world"
    - "phase mapper an ai platform to accelerate high throughput materials discovery"
    - "a neural algorithm of artistic style"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-4 (induced by source paper "label free supervision of neural networks with physics and domain knowledge"):
  with members:
    - "convexification of learning from constraints"
    - "fast training of triplet based deep binary embedding networks"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "adam a method for stochastic optimization"
    - "building high level features using large scale unsupervised 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.AI(Artificial Intelligence)