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: "convolution by evolution differentiable pattern producing networks"
  Abstract: In this work we introduce a differentiable version of the Compositional Pattern Producing Network, called the DPPN. Unlike a standard CPPN, the topology of a DPPN is evolved but the weights are learned. A Lamarckian algorithm, that combines evolution and learning, produces DPPNs to reconstruct an image. Our main result is that DPPNs can be evolved/trained to compress the weights of a denoising autoencoder from 157684 to roughly 200 parameters, while achieving a reconstruction accuracy comparable to a fully connected network with more than two orders of magnitude more parameters. The regularization ability of the DPPN allows it to rediscover (approximate) convolutional network architectures embedded within a fully connected architecture. Such convolutional architectures are the current state of the art for many computer vision applications, so it is satisfying that DPPNs are capable of discovering this structure rather than having to build it in by design. DPPNs exhibit better generalization when tested on the Omniglot dataset after being trained on MNIST, than directly encoded fully connected autoencoders. DPPNs are therefore a new framework for integrating learning and evolution.

Connected through hyperedge HE-1 (induced by source paper "finding online neural update rules by learning to remember"):
  with members:
    - "backpropamine training self modifying neural networks with differentiable neuromodulated plasticity"
    - "dota 2 with large scale deep reinforcement learning"
    - "ai gas ai generating algorithms an alternate paradigm for producing general artificial intelligence"
    - "large scale adversarial representation learning"
    - "transformer xl attentive language models beyond a fixed length context"
    - "metalearned neural memory"
    - "does learning require memorization a short tale about a long tail"
    - "network of evolvable neural units evolving to learn at a synaptic level"

Connected through hyperedge HE-2 (induced by source paper "neuroevolution of neural network architectures using codeepneat and keras"):
  with members:
    - "evolutionary neural automl for deep learning"
    - "autoaugment learning augmentation policies from data"
    - "chexnet radiologist level pneumonia detection on chest x rays with deep learning"
    - "deep neuroevolution genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning"
    - "a survey on deep learning in medical image analysis"
    - "neural architecture search with reinforcement learning"
    - "deep speech 2 end to end speech recognition in english and mandarin"
    - "microsoft coco captions data collection and evaluation server"

Connected through hyperedge HE-3 (induced by source paper "a study of fitness landscapes for neuroevolution"):
  with members:
    - "evolving deep neural networks"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "first improvement vs best improvement local optima networks of nk landscapes"
    - "a study of nk landscapes basins and local optima networks"

Connected through hyperedge HE-4 (induced by source paper "text generation with exemplar based adaptive decoding"):
  with members:
    - "retrieve and refine improved sequence generation models for dialogue"
    - "backpropagating through structured argmax using a spigot"
    - "generating diverse and accurate visual captions by comparative adversarial learning"
    - "rational recurrences"
    - "bottom up abstractive summarization"
    - "learning joint semantic parsers from disjoint data"
    - "learning neural templates for text generation"
    - "contextual parameter generation for universal neural machine translation"

Connected through hyperedge HE-5 (induced by source paper "alphax exploring neural architectures with deep neural networks and monte carlo tree search"):
  with members:
    - "nas bench 101 towards reproducible neural architecture search"
    - "evaluating the search phase of neural architecture search"
    - "practical deep learning architecture optimization"
    - "regularized evolution for image classifier architecture search"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "neural architecture optimization"
    - "darts differentiable architecture search"

Connected through hyperedge HE-6 (induced by source paper "a particle swarm optimization based flexible convolutional autoencoder for image classification"):
  with members:
    - "evolving unsupervised deep neural networks for learning meaningful representations"
    - "large scale evolution of image classifiers"
    - "designing neural network architectures using reinforcement learning"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "deep clustered convolutional kernels"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "autoencoding with xcsf"):
  with members:
    - "evolving unsupervised deep neural networks for learning meaningful representations"
    - "weight agnostic neural networks"
    - "autoencoding with a learning classifier system initial results"
    - "evolutionary stochastic gradient descent for optimization of deep neural networks"
    - "population based training of neural networks"
    - "a cognitive architecture based on a learning classifier system with spiking classifiers"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "representation learning a review and new perspectives"

Connected through hyperedge HE-8 (induced by source paper "anodev2 a coupled neural ode evolution framework"):
  with members:
    - "anode unconditionally accurate memory efficient gradients for neural odes"
    - "stable architectures for deep neural networks"
    - "deep neural networks motivated by partial differential equations"
    - "nais net stable deep networks from non autonomous differential equations"
    - "beyond finite layer neural networks bridging deep architectures and numerical differential equations"
    - "identity mappings in deep residual networks"
    - "deep residual learning for image recognition"

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)