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: "the evolutionary origins of hierarchy"
  Abstract: Hierarchical organization—the recursive composition of sub-modules—is ubiquitous in biological networks, including neural, metabolic, ecological, and genetic regulatory networks, and in human-made systems, such as large organizations and the Internet. To date, most research on hierarchy in networks has been limited to quantifying this property. However, an open, important question in evolutionary biology is why hierarchical organization evolves in the first place. It has recently been shown that modularity evolves because of the presence of a cost for network connections. Here we investigate whether such connection costs also tend to cause a hierarchical organization of such modules. In computational simulations, we find that networks without a connection cost do not evolve to be hierarchical, even when the task has a hierarchical structure. However, with a connection cost, networks evolve to be both modular and hierarchical, and these networks exhibit higher overall performance and evolvability (i.e. faster adaptation to new environments). Additional analyses confirm that hierarchy independently improves adaptability after controlling for modularity. Overall, our results suggest that the same force–the cost of connections–promotes the evolution of both hierarchy and modularity, and that these properties are important drivers of network performance and adaptability. In addition to shedding light on the emergence of hierarchy across the many domains in which it appears, these findings will also accelerate future research into evolving more complex, intelligent computational brains in the fields of artificial intelligence and robotics.

Connected through hyperedge HE-1 (induced by source paper "guiding neuroevolution with structural objectives"):
  with members:
    - "regularized evolution for image classifier architecture search"
    - "improving exploration in evolution strategies for deep reinforcement learning via a population of novelty seeking agents"
    - "deep neuroevolution genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning"
    - "diffusion based neuromodulation can eliminate catastrophic forgetting in simple neural networks"
    - "very deep convolutional networks for large scale image recognition"
    - "evolution of a subsumption architecture neurocontroller"

Connected through hyperedge HE-2 (induced by source paper "evolution of hierarchical structure reuse in igem synthetic dna sequences"):
  with members:
    - "emergence and evolution of hierarchical structure in complex systems"
    - "the hourglass effect in hierarchical dependency networks"
    - "lexis an optimization framework for discovering the hierarchical structure of sequential data"
    - "evolution of a modular software network"

Connected through hyperedge HE-3 (induced by source paper "the emergence of canalization and evolvability in an open ended interactive evolutionary system"):
  with members:
    - "robots that can adapt like animals"
    - "illuminating search spaces by mapping elites"
    - "deep neural networks are easily fooled high confidence predictions for unrecognizable images"
    - "intriguing properties of neural networks"
    - "eye tracking evolutionary algorithm to minimize user fatigue in iec applied to interactive one max problem"

Connected through hyperedge HE-4 (induced by source paper "evolving multimodal robot behavior via many stepping stones with the combinatorial multi objective evolutionary algorithm"):
  with members:
    - "illuminating generalization in deep reinforcement learning through procedural level generation"
    - "reinforcement learning with unsupervised auxiliary tasks"
    - "learning to navigate in complex environments"
    - "robots that can adapt like animals"

Connected through hyperedge HE-5 (induced by source paper "emergence and evolution of hierarchical structure in complex systems"):
  with members:
    - "the hourglass effect in hierarchical dependency networks"
    - "lexis an optimization framework for discovering the hierarchical structure of sequential data"
    - "how transferable are features in deep neural networks"

Connected through hyperedge HE-6 (induced by source paper "diffusion based neuromodulation can eliminate catastrophic forgetting in simple neural networks"):
  with members:
    - "overcoming catastrophic forgetting in neural networks"
    - "end to end training of deep visuomotor policies"

Connected through hyperedge HE-7 (induced by source paper "the hourglass effect in hierarchical dependency networks"):
  with members:
    - "lexis an optimization framework for discovering the hierarchical structure of sequential data"
    - "core periphery structure in networks"
    - "evolution of a modular software network"

Connected through hyperedge HE-8 (induced by source paper "lexis an optimization framework for discovering the hierarchical structure of sequential data"):
  with members:
    - "data representation and compression using linear programming approximations"
    - "the long and the short of it summarising event sequences with serial episodes"
    - "identifying hierarchical structure in sequences a linear time algorithm"

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)