human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "integrating deep learning with logic fusion for information extraction"):
  Abstract: Information extraction (IE) aims to produce structured information from an input text, e.g., Named Entity Recognition and Relation Extraction. Various attempts have been proposed for IE via feature engineering or deep learning. However, most of them fail to associate the complex relationships inherent in the task itself, which has proven to be especially crucial. For example, the relation between 2 entities is highly dependent on their entity types. These dependencies can be regarded as complex constraints that can be efficiently expressed as logical rules. To combine such logic reasoning capabilities with learning capabilities of deep neural networks, we propose to integrate logical knowledge in the form of first-order logic into a deep learning system, which can be trained jointly in an end-to-end manner. The integrated framework is able to enhance neural outputs with knowledge regularization via logic rules, and at the same time update the weights of logic rules to comply with the characteristics of the training data. We demonstrate the effectiveness and generalization of the proposed model on multiple IE tasks.

Includes members:
  Member "neural logic machines":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "relational neural machines")
  Member "deepproblog neural probabilistic logic programming":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "analyzing differentiable fuzzy logic operators")
  Member "double embeddings and cnn based sequence labeling for aspect extraction":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "snippext semi supervised opinion mining with augmented data")
  Member "deep probabilistic logic a unifying framework for indirect supervision":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "evidence sentence extraction for machine reading comprehension")
  Member "joint entity recognition and relation extraction as a multi head selection problem":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deeper task specificity improves joint entity and relation extraction")
  Member "adversarially regularising neural nli models to integrate logical background knowledge":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "stance detection benchmark how robust is your stance detection")
  Member "adversarial training for multi context joint entity and relation extraction":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "span based joint entity and relation extraction with transformer pre training")
  Member "attention is all you need":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")

Question: Please tell me which class the source paper 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)