human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "self delimiting neural networks"):
  Abstract: Self-delimiting (SLIM) programs are a central concept of theoretical computer science, particularly algorithmic information & probability theory, and asymptotically optimal program search (AOPS). To apply AOPS to (possibly recurrent) neural networks (NNs), I introduce SLIM NNs. Neurons of a typical SLIM NN have threshold activation functions. During a computational episode, activations are spreading from input neurons through the SLIM NN until the computation activates a special halt neuron. Weights of the NN's used connections define its program. Halting programs form a prefix code. The reset of the initial NN state does not cost more than the latest program execution. Since prefixes of SLIM programs influence their suffixes (weight changes occurring early in an episode influence which weights are considered later), SLIM NN learning algorithms (LAs) should execute weight changes online during activation spreading. This can be achieved by applying AOPS to growing SLIM NNs. To efficiently teach a SLIM NN to solve many tasks, such as correctly classifying many different patterns, or solving many different robot control tasks, each connection keeps a list of tasks it is used for. The lists may be efficiently updated during training. To evaluate the overall effect of currently tested weight changes, a SLIM NN LA needs to re-test performance only on the efficiently computable union of tasks potentially affected by the current weight changes. Future SLIM NNs will be implemented on 3-dimensional brain-like multi-processor hardware. Their LAs will minimize task-specific total wire length of used connections, to encourage efficient solutions of subtasks by subsets of neurons that are physically close. The novel class of SLIM NN LAs is currently being probed in ongoing experiments to be reported in separate papers.

Includes members:
  Member "multi column deep neural networks for image classification":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "importance driven deep learning system testing")
  Member "powerplay training an increasingly general problem solver by continually searching for the simplest still unsolvable problem":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "curriculum learning for reinforcement learning domains a framework and survey")
  Member "new millennium ai and the convergence of history":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "can intelligence explode")
  Member "optimal ordered problem solver":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "automl zero evolving machine learning algorithms from scratch")
  Member "reinforcement learning a survey":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "online fast adaptation and knowledge accumulation a new approach to continual learning")

Question: Please tell me which class the source paper 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)