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: "meta learning by the baldwin effect"
  Abstract: The scope of the Baldwin effect was recently called into question by two papers that closely examined the seminal work of Hinton and Nowlan. To this date there has been no demonstration of its necessity in empirically challenging tasks. Here we show that the Baldwin effect is capable of evolving few-shot supervised and reinforcement learning mechanisms, by shaping the hyperparameters and the initial parameters of deep learning algorithms. Furthermore it can genetically accommodate strong learning biases on the same set of problems as a recent machine learning algorithm called MAML "Model Agnostic Meta-Learning" which uses second-order gradients instead of evolution to learn a set of reference parameters (initial weights) that can allow rapid adaptation to tasks sampled from a distribution. Whilst in simple cases MAML is more data efficient than the Baldwin effect, the Baldwin effect is more general in that it does not require gradients to be backpropagated to the reference parameters or hyperparameters, and permits effectively any number of gradient updates in the inner loop. The Baldwin effect learns strong learning dependent biases, rather than purely genetically accommodating fixed behaviours in a learning independent manner.

Connected through hyperedge HE-1 (induced by source paper "rapidly adaptable legged robots via evolutionary meta learning"):
  with members:
    - "es maml simple hessian free meta learning"
    - "active domain randomization"
    - "efficient off policy meta reinforcement learning via probabilistic context variables"
    - "varibad a very good method for bayes adaptive deep rl via meta learning"
    - "multi agent manipulation via locomotion using hierarchical sim2real"
    - "norml no reward meta learning"
    - "learning generalizable locomotion skills with hierarchical reinforcement learning"
    - "learning agile and dynamic motor skills for legged robots"

Connected through hyperedge HE-2 (induced by source paper "neuro evolutionary frameworks for generalized learning agents"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "task agnostic dynamics priors for deep reinforcement learning"
    - "model based reinforcement learning for atari"
    - "investigating human priors for playing video games"
    - "a self replication basis for designing complex agents"
    - "hdltex hierarchical deep learning for text classification"
    - "a survey on multi task learning"
    - "concrete problems in ai safety"

Connected through hyperedge HE-3 (induced by source paper "adapting to unseen environments through explicit representation of context"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "procedural content generation from automatically generating game levels to increasing generality in machine learning"
    - "paired open ended trailblazer poet endlessly generating increasingly complex and diverse learning environments and their solutions"
    - "learning curriculum policies for reinforcement learning"
    - "automated curriculum learning by rewarding temporally rare events"
    - "lstm a search space odyssey"
    - "schema networks zero shot transfer with a generative causal model of intuitive physics"
    - "model agnostic meta learning for fast adaptation of deep networks"

Connected through hyperedge HE-4 (induced by source paper "meta world a benchmark and evaluation for multi task and meta reinforcement learning"):
  with members:
    - "efficient off policy meta reinforcement learning via probabilistic context variables"
    - "kpam keypoint affordances for category level robotic manipulation"
    - "ray interference a source of plateaus in deep reinforcement learning"
    - "habitat a platform for embodied ai research"
    - "deepmind control suite"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "learning dexterous in hand manipulation"
    - "visual reinforcement learning with imagined goals"

Connected through hyperedge HE-5 (induced by source paper "es maml simple hessian free meta learning"):
  with members:
    - "provably robust blackbox optimization for reinforcement learning"
    - "alpha maml adaptive model agnostic meta learning"
    - "norml no reward meta learning"
    - "evolvability es scalable and direct optimization of evolvability"
    - "structured monte carlo sampling for nonisotropic distributions via determinantal point processes"
    - "deep neuroevolution of recurrent and discrete world models"
    - "structured evolution with compact architectures for scalable policy optimization"
    - "simple random search provides a competitive approach to reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "meta learnt priors slow down catastrophic forgetting in neural networks"):
  with members:
    - "backpropamine training self modifying neural networks with differentiable neuromodulated plasticity"
    - "online meta learning"
    - "meta learning representations for continual learning"
    - "rapid learning or feature reuse towards understanding the effectiveness of maml"
    - "on first order meta learning algorithms"
    - "meta continual learning"
    - "re evaluating continual learning scenarios a categorization and case for strong baselines"
    - "born to learn the inspiration progress and future of evolved plastic artificial neural networks"

Connected through hyperedge HE-7 (induced by source paper "evolving self supervised neural networks autonomous intelligence from evolved self teaching"):
  with members:
    - "evolving self taught neural networks the baldwin effect and the emergence of intelligence"
    - "born to learn the inspiration progress and future of evolved plastic artificial neural networks"
    - "innateness alphazero and artificial intelligence"
    - "deep neuroevolution genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning"
    - "multi agent actor critic for mixed cooperative competitive environments"
    - "deep learning in neural networks"

Connected through hyperedge HE-8 (induced by source paper "evolving intrinsic motivations for altruistic behavior"):
  with members:
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "inequity aversion improves cooperation in intertemporal social dilemmas"
    - "maintaining cooperation in complex social dilemmas using deep reinforcement learning"
    - "evolved policy gradients"
    - "a multi agent reinforcement learning model of common pool resource appropriation"
    - "learning with opponent learning awareness"
    - "improving exploration in evolution strategies for deep reinforcement learning via a population of novelty seeking agents"

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)