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: "improving information extraction by acquiring external evidence with reinforcement learning"
  Abstract: Most successful information extraction systems operate with access to a large collection of documents. In this work, we explore the task of acquiring and incorporating external evidence to improve extraction accuracy in domains where the amount of training data is scarce. This process entails issuing search queries, extraction from new sources and reconciliation of extracted values, which are repeated until sufficient evidence is collected. We approach the problem using a reinforcement learning framework where our model learns to select optimal actions based on contextual information. We employ a deep Q-network, trained to optimize a reward function that reflects extraction accuracy while penalizing extra effort. Our experiments on two databases -- of shooting incidents, and food adulteration cases -- demonstrate that our system significantly outperforms traditional extractors and a competitive meta-classifier baseline.

Connected through hyperedge HE-1 (induced by source paper "weakly supervised video summarization by hierarchical reinforcement learning"):
  with members:
    - "rethinking the evaluation of video summaries"
    - "fully convolutional network with multi step reinforcement learning for image processing"
    - "crafting a toolchain for image restoration by deep reinforcement learning"
    - "deep reinforcement learning for unsupervised video summarization with diversity representativeness reward"
    - "feudal networks for hierarchical reinforcement learning"
    - "learning policies for adaptive tracking with deep feature cascades"
    - "deep reinforcement learning for visual object tracking in videos"
    - "deep reinforcement learning with a combinatorial action space for predicting popular reddit threads"

Connected through hyperedge HE-2 (induced by source paper "actively seeking and learning from live data"):
  with members:
    - "on first order meta learning algorithms"
    - "deep nets what have they ever done for vision"
    - "natural language to structured query generation via meta learning"
    - "deep learning a critical appraisal"
    - "overcoming language priors in visual question answering with adversarial regularization"
    - "one shot imitation from observing humans via domain adaptive meta learning"
    - "an empirical evaluation of visual question answering for novel objects"
    - "visual question answering as a meta learning task"

Connected through hyperedge HE-3 (induced by source paper "dialog state tracking with reinforced data augmentation"):
  with members:
    - "improving dialogue state tracking by discerning the relevant context"
    - "deep communicating agents for abstractive summarization"
    - "dsgan generative adversarial training for distant supervision relation extraction"
    - "reinforced co training"
    - "contextual augmentation data augmentation by words with paradigmatic relations"
    - "robust distant supervision relation extraction via deep reinforcement learning"
    - "towards universal dialogue state tracking"
    - "data augmentation for spoken language understanding via joint variational generation"

Connected through hyperedge HE-4 (induced by source paper "multi hop reading comprehension via deep reinforcement learning based document traversal"):
  with members:
    - "variational reasoning for question answering with knowledge graph"
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"
    - "making neural qa as simple as possible but not simpler"
    - "multi mention learning for reading comprehension with neural cascades"
    - "reinforced mnemonic reader for machine reading comprehension"
    - "linguistic knowledge as memory for recurrent neural networks"
    - "reading wikipedia to answer open domain questions"
    - "proximal policy optimization algorithms"

Connected through hyperedge HE-5 (induced by source paper "learning representations and agents for information retrieval"):
  with members:
    - "an updated duet model for passage re ranking"
    - "hierarchical neural story generation"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "evidence aggregation for answer re ranking in open domain question answering"
    - "r 3 reinforced reader ranker for open domain question answering"
    - "improving exploration in evolution strategies for deep reinforcement learning via a population of novelty seeking agents"
    - "a deep reinforcement learning chatbot"
    - "bidirectional attention flow for machine comprehension"

Connected through hyperedge HE-6 (induced by source paper "latent retrieval for weakly supervised open domain question answering"):
  with members:
    - "end to end open domain question answering with"
    - "ranking paragraphs for improving answer recall in open domain question answering"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "an efficient framework for learning sentence representations"
    - "evaluation of sentence embeddings in downstream and linguistic probing tasks"
    - "adaptive document retrieval for deep question answering"
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"

Connected through hyperedge HE-7 (induced by source paper "on the possibility of rewarding structure learning agents mutual information on linguistic random sets"):
  with members:
    - "miss tools and mr fruit emergent communication in agents learning about object affordances"
    - "a meta transfer objective for learning to disentangle causal mechanisms"
    - "a survey of reinforcement learning informed by natural language"
    - "diversity is all you need learning skills without a reward function"
    - "mihash online hashing with mutual information"
    - "rl 2 fast reinforcement learning via slow reinforcement learning"
    - "unsupervised sentence representations as word information series revisiting tf idf"
    - "deep reinforcement learning for dialogue generation"

Connected through hyperedge HE-8 (induced by source paper "learning dynamic context augmentation for global entity linking"):
  with members:
    - "neural collective entity linking based on recurrent random walk network learning"
    - "relation mention extraction from noisy data with hierarchical reinforcement learning"
    - "improving entity linking by modeling latent relations between mentions"
    - "collective entity disambiguation with structured gradient tree boosting"
    - "deeptype multilingual entity linking by neural type system evolution"
    - "pair linking for collective entity disambiguation two could be better than all"
    - "deep joint entity disambiguation with local neural attention"
    - "capturing semantic similarity for entity linking with convolutional neural networks"

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)