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: "representation stability as a regularizer for improved text analytics transfer learning"
  Abstract: Although neural networks are well suited for sequential transfer learning tasks, the catastrophic forgetting problem hinders proper integration of prior knowledge. In this work, we propose a solution to this problem by using a multi-task objective based on the idea of distillation and a mechanism that directly penalizes forgetting at the shared representation layer during the knowledge integration phase of training. We demonstrate our approach on a Twitter domain sentiment analysis task with sequential knowledge transfer from four related tasks.  We show that our technique outperforms networks fine-tuned to the target task. Additionally, we show both through empirical evidence and examples that it does not forget useful knowledge from the source task that is forgotten during standard fine-tuning. Surprisingly, we find that first distilling a human made rule based sentiment engine into a recurrent neural network and then integrating the knowledge with the target task data leads to a substantial gain in generalization performance. Our experiments demonstrate the power of multi-source transfer techniques in practical text analytics problems when paired with distillation. In particular, for the SemEval 2016 Task 4 Subtask A (Nakov et al., 2016) dataset we surpass the state of the art established during the competition with a comparatively simple model architecture that is not even competitive when trained on only the labeled task specific data.

Connected through hyperedge HE-1 (induced by source paper "mix review alleviate forgetting in the pretrain finetune framework for neural language generation models"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "transfertransfo a transfer learning approach for neural network based conversational agents"
    - "an empirical evaluation of multi task learning in deep neural networks for natural language processing"
    - "the curious case of neural text degeneration"
    - "pretraining methods for dialog context representation learning"
    - "do neural dialog systems use the conversation history effectively an empirical study"
    - "real or fake learning to discriminate machine from human generated text"
    - "mass masked sequence to sequence pre training for language generation"

Connected through hyperedge HE-2 (induced by source paper "on the role of weight sharing during deep option learning"):
  with members:
    - "routing networks and the challenges of modular and compositional computation"
    - "reinforcement learning without backpropagation or a clock"
    - "learning abstract options"
    - "how many random seeds statistical power analysis in deep reinforcement learning experiments"
    - "learning to learn without forgetting by maximizing transfer and minimizing interference"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "progressive neural networks"
    - "routing networks adaptive selection of non linear functions for multi task learning"

Connected through hyperedge HE-3 (induced by source paper "forget me not reducing catastrophic forgetting for domain adaptation in reading comprehension"):
  with members:
    - "the natural language decathlon multitask learning as question answering"
    - "know what you don t know unanswerable questions for squad"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "learning to learn without forgetting by maximizing transfer and minimizing interference"
    - "overcoming catastrophic forgetting with hard attention to the task"
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"
    - "attention is all you need"

Connected through hyperedge HE-4 (induced by source paper "learning to learn without forgetting by maximizing transfer and minimizing interference"):
  with members:
    - "on first order meta learning algorithms"
    - "learning abstract options"
    - "reptile a scalable metalearning algorithm"
    - "riemannian walk for incremental learning understanding forgetting and intransigence"
    - "overcoming catastrophic forgetting with hard attention to the task"
    - "learning robust options"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "lifelong learning with dynamically expandable networks"

Connected through hyperedge HE-5 (induced by source paper "learning abstract options"):
  with members:
    - "automated curriculum learning for neural networks"
    - "learning to compose skills"
    - "progressive neural networks"
    - "routing networks adaptive selection of non linear functions for multi task learning"
    - "feudal networks for hierarchical reinforcement learning"
    - "learning to multi task by active sampling"
    - "learnings options end to end for continuous action tasks"
    - "pathnet evolution channels gradient descent in super neural networks"

Connected through hyperedge HE-6 (induced by source paper "scalable recollections for continual lifelong learning"):
  with members:
    - "automated curriculum learning for neural networks"
    - "knowledge distillation using unlabeled mismatched images"
    - "progressive neural networks"
    - "lifelong learning with dynamically expandable networks"
    - "generative adversarial active learning"
    - "continual learning with deep generative replay"
    - "neural episodic control"
    - "learning to remember rare events"

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.CL(Computation and Language)