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: "pathnet evolution channels gradient descent in super neural networks"
  Abstract: For artificial general intelligence (AGI) it would be efficient if multiple users trained the same giant neural network, permitting parameter reuse, without catastrophic forgetting. PathNet is a first step in this direction. It is a neural network algorithm that uses agents embedded in the neural network whose task is to discover which parts of the network to re-use for new tasks. Agents are pathways (views) through the network which determine the subset of parameters that are used and updated by the forwards and backwards passes of the backpropogation algorithm. During learning, a tournament selection genetic algorithm is used to select pathways through the neural network for replication and mutation. Pathway fitness is the performance of that pathway measured according to a cost function. We demonstrate successful transfer learning; fixing the parameters along a path learned on task A and re-evolving a new population of paths for task B, allows task B to be learned faster than it could be learned from scratch or after fine-tuning. Paths evolved on task B re-use parts of the optimal path evolved on task A. Positive transfer was demonstrated for binary MNIST, CIFAR, and SVHN supervised learning classification tasks, and a set of Atari and Labyrinth reinforcement learning tasks, suggesting PathNets have general applicability for neural network training. Finally, PathNet also significantly improves the robustness to hyperparameter choices of a parallel asynchronous reinforcement learning algorithm (A3C).

Connected through hyperedge HE-1 (induced by source paper "review analyze and design a comprehensive deep reinforcement learning framework"):
  with members:
    - "deep reinforcement learning for cyber security"
    - "pytorch an imperative style high performance deep learning library"
    - "dopamine a research framework for deep reinforcement learning"
    - "deep reinforcement learning for multi agent systems a review of challenges solutions and applications"
    - "large scale study of curiosity driven learning"
    - "accelerated methods for deep reinforcement learning"
    - "relational recurrent neural networks"
    - "distributed prioritized experience replay"

Connected through hyperedge HE-2 (induced by source paper "meta learning curiosity algorithms"):
  with members:
    - "evolving loss functions with multivariate taylor polynomial parameterizations"
    - "few shot bayesian imitation learning with logical program policies"
    - "ai gas ai generating algorithms an alternate paradigm for producing general artificial intelligence"
    - "self supervised exploration via disagreement"
    - "few shot bayesian imitation learning with logic over programs"
    - "discovery of useful questions as auxiliary tasks"
    - "world discovery models"
    - "weight agnostic neural networks"

Connected through hyperedge HE-3 (induced by source paper "on catastrophic interference in atari 2600 games"):
  with members:
    - "go explore a new approach for hard exploration problems"
    - "continual unsupervised representation learning"
    - "continual lifelong learning with neural networks a review"
    - "ray interference a source of plateaus in deep reinforcement learning"
    - "benchmarking bonus based exploration methods on the arcade learning environment"
    - "dopamine a research framework for deep reinforcement learning"
    - "exploration by random network distillation"
    - "task free continual learning"

Connected through hyperedge HE-4 (induced by source paper "gradient surgery for multi task learning"):
  with members:
    - "distilling policy distillation"
    - "regularized hierarchical policies for compositional transfer in robotics"
    - "branched multi task networks deciding what layers to share"
    - "meta world a benchmark and evaluation for multi task and meta reinforcement learning"
    - "routing networks and the challenges of modular and compositional computation"
    - "ray interference a source of plateaus in deep reinforcement learning"
    - "attentive single tasking of multiple tasks"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"

Connected through hyperedge HE-5 (induced by source paper "learning to continually learn"):
  with members:
    - "backpropamine training self modifying neural networks with differentiable neuromodulated plasticity"
    - "ai gas ai generating algorithms an alternate paradigm for producing general artificial intelligence"
    - "meta learning representations for continual learning"
    - "differentiable plasticity training plastic neural networks with backpropagation"
    - "continuous learning in single incremental task scenarios"
    - "rotate your networks better weight consolidation and less catastrophic forgetting"
    - "born to learn the inspiration progress and future of evolved plastic artificial neural networks"
    - "explicit inductive bias for transfer learning with convolutional networks"

Connected through hyperedge HE-6 (induced by source paper "structured compression and sharing of representational space for continual learning"):
  with members:
    - "learn to grow a continual structure learning framework for overcoming catastrophic forgetting"
    - "continual lifelong learning with neural networks a review"
    - "gradient based sample selection for online continual learning"
    - "re evaluating continual learning scenarios a categorization and case for strong baselines"
    - "tree cnn a deep convolutional neural network for lifelong learning"
    - "a low effort approach to structured cnn design using pca"
    - "alleviating catastrophic forgetting using context dependent gating and synaptic stabilization"
    - "generative replay with feedback connections as a general strategy for continual learning"

Connected through hyperedge HE-7 (induced by source paper "ternary feature masks continual learning without any forgetting"):
  with members:
    - "class incremental learning via deep model consolidation"
    - "learn to grow a continual structure learning framework for overcoming catastrophic forgetting"
    - "continual learning a comparative study on how to defy forgetting in classification tasks"
    - "rotate your networks better weight consolidation and less catastrophic forgetting"
    - "efficient lifelong learning with a gem"
    - "piggyback adapting a single network to multiple tasks by learning to mask weights"
    - "learning to learn without forgetting by maximizing transfer and minimizing interference"
    - "riemannian walk for incremental learning understanding forgetting and intransigence"

Connected through hyperedge HE-8 (induced by source paper "revisiting spatial invariance with low rank local connectivity"):
  with members:
    - "condconv conditionally parameterized convolutions for efficient inference"
    - "stand alone self attention in vision models"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "assessing the scalability of biologically motivated deep learning algorithms and architectures"
    - "attention branch network learning of attention mechanism for visual explanation"
    - "learn to pay attention"
    - "cbam convolutional block attention module"
    - "you look twice gaternet for dynamic filter selection in cnns"

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)