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: "deep reinforcement learning for multi objective optimization"
  Abstract: This study proposes an end-to-end framework for solving multi-objective optimization problems (MOPs) using Deep Reinforcement Learning (DRL), termed DRL-MOA. The idea of decomposition is adopted to decompose a MOP into a set of scalar optimization subproblems. The subproblems are then optimized cooperatively by a neighbourhood-based parameter transfer strategy which significantly accelerates the training procedure and makes the realization of DRL-MOA possible. The subproblems are modelled as neural networks and the RL method is used to optimize them. In specific, the multi-objective travelling salesman problem (MOTSP) is solved in this work using the DRL-MOA framework by modelling the subproblem as the Pointer Network. It is found that, once the trained model is available, it can scale to MOTSPs of any number of cities, e.g., 70-city, 100-city, even the 200-city MOTSP, without re-training the model. The Pareto Front can be directly obtained by a simple feed-forward of the network; thereby, no iteration is required and the MOP can be always solved in a reasonable time. Experimental results indicate a strong convergence ability of the DRL-MOA, especially for large-scale MOTSPs, e.g., 200-city MOTSP, for which evolutionary algorithms such as NSGA-II and MOEA/D are pretty hard to converge even implemented for a large number of iterations. The DRL-MOA can also obtain a much wider spread of the PF than the two competitors. Moreover, the DRL-MOA has a high level of modularity and can be easily generalized to other MOPs by replacing the modelling of the subproblem.

Connected through hyperedge HE-1 (induced by source paper "modrl d am multiobjective deep reinforcement learning algorithm using decomposition and attention model for multiobjective optimization"):
  with members:
    - "attention is all you need"
    - "learning combinatorial optimization algorithms over graphs"
    - "neural combinatorial optimization with reinforcement learning"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "neural machine translation by jointly learning to align and translate"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

Connected through hyperedge HE-2 (induced by source paper "rapidlayout fast hard block placement of fpga optimized systolic arrays using evolutionary algorithms"):
  with members:
    - "the deep learning revolution and its implications for computer architecture and chip design"
    - "nsga net neural architecture search using multi objective genetic algorithm"
    - "in datacenter performance analysis of a tensor processing unit"
    - "cma es for hyperparameter optimization of deep neural networks"

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)