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: "scalability of using restricted boltzmann machines for combinatorial optimization"
  Abstract: Estimation of Distribution Algorithms (EDAs) require flexible probability models that can be efficiently learned and sampled. Restricted Boltzmann Machines (RBMs) are generative neural networks with these desired properties. We integrate an RBM into an EDA and evaluate the performance of this system in solving combinatorial optimization problems with a single objective. We assess how the number of fitness evaluations and the CPU time scale with problem size and with problem complexity. The results are compared to the Bayesian Optimization Algorithm, a state-of-the-art EDA. Although RBM-EDA requires larger population sizes and a larger number of fitness evaluations, it outperforms BOA in terms of CPU times, in particular if the problem is large or complex. RBM-EDA requires less time for model building than BOA. These results highlight the potential of using generative neural networks for combinatorial optimization.

Connected through hyperedge HE-1 (induced by source paper "learning fine grained search space pruning and heuristics for combinatorial optimization"):
  with members:
    - "learning multi stage sparsification for maximum clique enumeration"
    - "fine grained search space classification for hard enumeration variants of subset problems"
    - "learning heuristics over large graphs via deep reinforcement learning"
    - "learning combinatorial optimization algorithms over graphs"
    - "neural combinatorial optimization with reinforcement learning"
    - "fast algorithms for the maximum clique problem on massive graphs with applications to overlapping community detection"
    - "practical graph isomorphism ii"

Connected through hyperedge HE-2 (induced by source paper "deep learning in business analytics and operations research models applications and managerial implications"):
  with members:
    - "putting question answering systems into practice transfer learning for efficient domain customization"
    - "sentiment analysis based on rhetorical structure theory learning deep neural networks from discourse trees"
    - "forecasting remaining useful life interpretable deep learning approach via variational bayesian inferences"
    - "similarity encoding for learning with dirty categorical variables"
    - "decision support with text based emotion recognition deep learning for affective computing"
    - "deep learning for affective computing text based emotion recognition in decision support"
    - "decision support from financial disclosures with deep neural networks and transfer learning"
    - "deep embedding forest forest based serving with deep embedding features"

Connected through hyperedge HE-3 (induced by source paper "denoising autoencoders for fast combinatorial black box optimization"):
  with members:
    - "sequence to sequence learning with neural networks"
    - "generalized denoising auto encoders as generative models"
    - "what regularized auto encoders learn from the data generating distribution"
    - "improving neural networks by preventing co adaptation of feature detectors"
    - "analysis of estimation of distribution algorithms and genetic algorithms on nk landscapes"

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.NE(Neural and Evolutionary Computing)