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 surprising creativity of digital evolution a collection of anecdotes from the evolutionary computation and artificial life research communities"
  Abstract: Biological evolution provides a creative fount of complex and subtle adaptations, often surprising the scientists who discover them. However, because evolution is an algorithmic process that transcends the substrate in which it occurs, evolution's creativity is not limited to nature. Indeed, many researchers in the field of digital evolution have observed their evolving algorithms and organisms subverting their intentions, exposing unrecognized bugs in their code, producing unexpected adaptations, or exhibiting outcomes uncannily convergent with ones in nature. Such stories routinely reveal creativity by evolution in these digital worlds, but they rarely fit into the standard scientific narrative. Instead they are often treated as mere obstacles to be overcome, rather than results that warrant study in their own right. The stories themselves are traded among researchers through oral tradition, but that mode of information transmission is inefficient and prone to error and outright loss. Moreover, the fact that these stories tend to be shared only among practitioners means that many natural scientists do not realize how interesting and lifelike digital organisms are and how natural their evolution can be. To our knowledge, no collection of such anecdotes has been published before. This paper is the crowd-sourced product of researchers in the fields of artificial life and evolutionary computation who have provided first-hand accounts of such cases. It thus serves as a written, fact-checked collection of scientifically important and even entertaining stories. In doing so we also present here substantial evidence that the existence and importance of evolutionary surprises extends beyond the natural world, and may indeed be a universal property of all complex evolving systems.

Connected through hyperedge HE-1 (induced by source paper "effective reinforcement learning through evolutionary surrogate assisted prescription"):
  with members:
    - "quantifying point prediction uncertainty in neural networks via residual estimation with an i o kernel"
    - "creative ai through evolutionary computation"
    - "a generalized algorithm for multi objective reinforcement learning and policy adaptation"
    - "evolved policy gradients"
    - "recurrent world models facilitate policy evolution"
    - "deep neuroevolution genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning"
    - "emergence of locomotion behaviours in rich environments"
    - "proximal policy optimization algorithms"

Connected through hyperedge HE-2 (induced by source paper "go explore a new approach for hard exploration problems"):
  with members:
    - "fractal ai a fragile theory of intelligence"
    - "exploration by random network distillation"
    - "backplay man muss immer umkehren"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "learning dexterous in hand manipulation"
    - "fast exploration with simplified models and approximately optimistic planning in model based reinforcement learning"
    - "deep curiosity search intra life exploration can improve performance on challenging deep reinforcement learning problems"
    - "playing hard exploration games by watching youtube"

Connected through hyperedge HE-3 (induced by source paper "emergent tool use from multi agent autocurricula"):
  with members:
    - "emergent coordination through competition"
    - "autocurricula and the emergence of innovation from social interaction a manifesto for multi agent intelligence research"
    - "the tools challenge rapid trial and error learning in physical problem solving"
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "improvisation through physical understanding using novel objects as tools with visual foresight"
    - "paired open ended trailblazer poet endlessly generating increasingly complex and diverse learning environments and their solutions"
    - "structured agents for physical construction"
    - "exploration by random network distillation"

Connected through hyperedge HE-4 (induced by source paper "learning to predict without looking ahead world models without forward prediction"):
  with members:
    - "weight agnostic neural networks"
    - "reinforcement learning for improving agent design"
    - "model based reinforcement learning for atari"
    - "videoflow a flow based generative model for video"
    - "deep neuroevolution of recurrent and discrete world models"
    - "learning latent dynamics for planning from pixels"
    - "visual foresight model based deep reinforcement learning for vision based robotic control"
    - "lipschitz continuity in model based reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "reward tampering problems and solutions in reinforcement learning a causal influence diagram perspective"):
  with members:
    - "categorizing wireheading in partially embedded agents"
    - "model based reinforcement learning for atari"
    - "understanding agent incentives using causal influence diagrams part i single action settings"
    - "modeling agi safety frameworks with causal influence diagrams"
    - "neural predictive belief representations"
    - "agi safety literature review"
    - "visual foresight model based deep reinforcement learning for vision based robotic control"
    - "world models"

Connected through hyperedge HE-6 (induced by source paper "are we making real progress in simulated environments measuring the sim2real gap in embodied visual navigation"):
  with members:
    - "emergent tool use from multi agent autocurricula"
    - "fastdepth fast monocular depth estimation on embedded systems"
    - "decentralized distributed ppo solving pointgoal navigation"
    - "benchmarking classic and learned navigation in complex 3d environments"
    - "splitnet sim2sim and task2task transfer for embodied visual navigation"
    - "pyrobot an open source robotics framework for research and benchmarking"
    - "learning agile and dynamic motor skills for legged robots"
    - "to learn or not to learn analyzing the role of learning for navigation in virtual environments"

Connected through hyperedge HE-7 (induced by source paper "evolutionary computation and ai safety research problems impeding routine and safe real world application of evolution"):
  with members:
    - "paired open ended trailblazer poet endlessly generating increasingly complex and diverse learning environments and their solutions"
    - "agi safety literature review"
    - "born to learn the inspiration progress and future of evolved plastic artificial neural networks"
    - "supervising strong learners by amplifying weak experts"
    - "a study on overfitting in deep reinforcement learning"
    - "scalable agent alignment via reward modeling a research direction"
    - "quantifying generalization in reinforcement learning"
    - "incomplete contracting and ai alignment"

Connected through hyperedge HE-8 (induced by source paper "understanding agent incentives using causal influence diagrams part i single action settings"):
  with members:
    - "modeling agi safety frameworks with causal influence diagrams"
    - "agi safety literature review"
    - "the measure and mismeasure of fairness a critical review of fair machine learning"
    - "ai safety gridworlds"
    - "on formalizing fairness in prediction with machine learning"
    - "a game theoretic analysis of the off switch game"
    - "good and safe uses of ai oracles"
    - "exposing the probabilistic causal structure of discrimination"

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)