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: "expert gate lifelong learning with a network of experts"
  Abstract: In this paper we introduce a model of lifelong learning, based on a Network of Experts. New tasks / experts are learned and added to the model sequentially, building on what was learned before. To ensure scalability of this process,data from previous tasks cannot be stored and hence is not available when learning a new task. A critical issue in such context, not addressed in the literature so far, relates to the decision which expert to deploy at test time. We introduce a set of gating autoencoders that learn a representation for the task at hand, and, at test time, automatically forward the test sample to the relevant expert. This also brings memory efficiency as only one expert network has to be loaded into memory at any given time. Further, the autoencoders inherently capture the relatedness of one task to another, based on which the most relevant prior model to be used for training a new expert, with finetuning or learning without-forgetting, can be selected. We evaluate our method on image classification and video prediction problems.

Connected through hyperedge HE-1 (induced by source paper "attention over parameters for dialogue systems"):
  with members:
    - "the second conversational intelligence challenge convai2"
    - "transfertransfo a transfer learning approach for neural network based conversational agents"
    - "global to local memory pointer networks for task oriented dialogue"
    - "moel mixture of empathetic listeners"
    - "deepcopy grounded response generation with hierarchical pointer networks"
    - "caire an end to end empathetic chatbot"
    - "learning from dialogue after deployment feed yourself chatbot"
    - "transferable multi domain state generator for task oriented dialogue systems"

Connected through hyperedge HE-2 (induced by source paper "a neural dirichlet process mixture model for task free continual learning"):
  with members:
    - "learn to grow a continual structure learning framework for overcoming catastrophic forgetting"
    - "on tiny episodic memories in continual learning"
    - "continual lifelong learning with neural networks a review"
    - "gradient based sample selection for online continual learning"
    - "task free continual learning"
    - "re evaluating continual learning scenarios a categorization and case for strong baselines"
    - "reconciling meta learning and continual learning with online mixtures of tasks"
    - "continuous learning in single incremental task scenarios"

Connected through hyperedge HE-3 (induced by source paper "learning home crowdsourced training of large neural networks using decentralized mixture of experts"):
  with members:
    - "scaling laws for neural language models"
    - "zero memory optimization towards training a trillion parameter models"
    - "megatron lm training multi billion parameter language models using model parallelism"
    - "natural compression for distributed deep learning"
    - "defending against neural fake news"
    - "large scale learning of general visual representations for transfer"
    - "optimizing network performance for distributed dnn training on gpu clusters imagenet alexnet training in 1 5 minutes"
    - "pipemare asynchronous pipeline parallel dnn training"

Connected through hyperedge HE-4 (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-5 (induced by source paper "scail classifier weights scaling for class incremental learning"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "incremental learning with maximum entropy regularization rethinking forgetting and intransigence"
    - "growing a brain fine tuning by increasing model capacity"
    - "large scale incremental learning"
    - "m2kd multi model and multi level knowledge distillation for incremental learning"
    - "do better imagenet models transfer better"
    - "revisiting distillation and incremental classifier learning"
    - "deesil deep shallow incremental learning"

Connected through hyperedge HE-6 (induced by source paper "trained model fusion for object detection using gating network"):
  with members:
    - "adaptive weighting multi field of view cnn for semantic segmentation in pathology"
    - "yolov3 an incremental improvement"
    - "visual saliency prediction using a mixture of deep neural networks"
    - "partial adversarial domain adaptation"
    - "edf ensemble distill and fuse for easy video labeling"
    - "robust deep multi modal learning based on gated information fusion network"
    - "rank of experts detection network ensemble"
    - "focal loss for dense object detection"

Connected through hyperedge HE-7 (induced by source paper "targeted forgetting and false memory formation in continual learners through adversarial backdoor attacks"):
  with members:
    - "three scenarios for continual learning"
    - "continual learning a comparative study on how to defy forgetting in classification tasks"
    - "task free continual learning"
    - "efficient lifelong learning with a gem"
    - "poison frogs targeted clean label poisoning attacks on neural networks"
    - "memory aware synapses learning what not to forget"
    - "progressive neural networks"
    - "continual learning through synaptic intelligence"

Connected through hyperedge HE-8 (induced by source paper "uncertainty quantification for deep context aware mobile activity recognition and unknown context discovery"):
  with members:
    - "adaptive activity monitoring with uncertainty quantification in switching gaussian process models"
    - "deep spatially coherent inverse sensor models with uncertainty incorporation using the evidential framework"
    - "deep learning for sensor based activity recognition a survey"
    - "evidential deep learning to quantify classification uncertainty"
    - "cnn features are also great at unsupervised classification"
    - "learnable pooling with context gating for video classification"
    - "cnn features off the shelf an astounding baseline for recognition"
    - "decaf a deep convolutional activation feature for generic visual 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.CV(Computer Vision and Pattern Recognition)