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: "deep probabilistic logic a unifying framework for indirect supervision"
  Abstract: Deep learning has emerged as a versatile tool for a wide range of NLP tasks, due to its superior capacity in representation learning. But its applicability is limited by the reliance on annotated examples, which are difficult to produce at scale. Indirect supervision has emerged as a promising direction to address this bottleneck, either by introducing labeling functions to automatically generate noisy examples from unlabeled text, or by imposing constraints over interdependent label decisions. A plethora of methods have been proposed, each with respective strengths and limitations. Probabilistic logic offers a unifying language to represent indirect supervision, but end-to-end modeling with probabilistic logic is often infeasible due to intractable inference and learning. In this paper, we propose deep probabilistic logic (DPL) as a general framework for indirect supervision, by composing probabilistic logic with deep learning. DPL models label decisions as latent variables, represents prior knowledge on their relations using weighted first-order logical formulas, and alternates between learning a deep neural network for the end task and refining uncertain formula weights for indirect supervision, using variational EM. This framework subsumes prior indirect supervision methods as special cases, and enables novel combination via infusion of rich domain and linguistic knowledge. Experiments on biomedical machine reading demonstrate the promise of this approach.

Connected through hyperedge HE-1 (induced by source paper "evidence sentence extraction for machine reading comprehension"):
  with members:
    - "dream a challenge dataset and models for dialogue based reading comprehension"
    - "training a ranking function for open domain question answering"
    - "ukp athene multi sentence textual entailment for claim verification"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "neural models for reasoning over multiple mentions using coreference"
    - "know what you don t know unanswerable questions for squad"
    - "coqa a conversational question answering challenge"
    - "fever a large scale dataset for fact extraction and verification"

Connected through hyperedge HE-2 (induced by source paper "augmenting neural networks with first order logic"):
  with members:
    - "be consistent improving procedural text comprehension using label consistency"
    - "a unified model for extractive and abstractive summarization using inconsistency loss"
    - "backpropagating through structured argmax using a spigot"
    - "deep contextualized word representations"
    - "towards semi supervised learning for deep semantic role labeling"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "adversarially regularising neural nli models to integrate logical background knowledge"
    - "learning to generate reviews and discovering sentiment"

Connected through hyperedge HE-3 (induced by source paper "adversarial analysis of natural language inference systems"):
  with members:
    - "right for the wrong reasons diagnosing syntactic heuristics in natural language inference"
    - "assessing bert s syntactic abilities"
    - "inoculation by fine tuning a method for analyzing challenge datasets"
    - "multi task deep neural networks for natural language understanding"
    - "annotation artifacts in natural language inference data"
    - "breaking nli systems with sentences that require simple lexical inferences"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "analyzing compositionality sensitivity of nli models"

Connected through hyperedge HE-4 (induced by source paper "integrating deep learning with logic fusion for information extraction"):
  with members:
    - "neural logic machines"
    - "deepproblog neural probabilistic logic programming"
    - "double embeddings and cnn based sequence labeling for aspect extraction"
    - "joint entity recognition and relation extraction as a multi head selection problem"
    - "adversarially regularising neural nli models to integrate logical background knowledge"
    - "adversarial training for multi context joint entity and relation extraction"
    - "attention is all you need"
    - "deep semantic role labeling with self attention"

Connected through hyperedge HE-5 (induced by source paper "learning to denoise distantly labeled data for entity typing"):
  with members:
    - "imposing label relational inductive bias for extremely fine grained entity typing"
    - "ultra fine entity typing"
    - "deep contextualized word representations"
    - "hierarchical losses and new resources for fine grained entity typing and linking"
    - "end to end neural coreference resolution"
    - "learning with noise enhance distantly supervised relation extraction with dynamic transition matrix"
    - "snorkel rapid training data creation with weak supervision"
    - "multi level representations for fine grained typing of knowledge base entities"

Connected through hyperedge HE-6 (induced by source paper "document level n ary relation extraction with multiscale representation learning"):
  with members:
    - "simultaneously self attending to all mentions for full abstract biological relation extraction"
    - "n ary relation extraction using graph state lstm"
    - "fonduer knowledge base construction from richly formatted data"
    - "cross sentence n ary relation extraction with graph lstms"
    - "attention is all you need"
    - "distant supervision for relation extraction beyond the sentence boundary"
    - "joint extraction of events and entities within a document context"
    - "hierarchical multiscale recurrent neural networks"

Connected through hyperedge HE-7 (induced by source paper "modeling language variation and universals a survey on typological linguistics for natural language processing"):
  with members:
    - "morphological word embeddings"
    - "from phonology to syntax unsupervised linguistic typology at different levels with language embeddings"
    - "xnli evaluating cross lingual sentence representations"
    - "a deep generative model of vowel formant typology"
    - "deep contextualized word representations"
    - "adversarial propagation and zero shot cross lingual transfer of word vector specialization"
    - "past present future a computational investigation of the typology of tense in 1000 languages"
    - "unsupervised neural machine translation"

Connected through hyperedge HE-8 (induced by source paper "search guided lightly supervised training of structured prediction energy networks"):
  with members:
    - "deep value networks learn to evaluate and iteratively refine structured outputs"
    - "snorkel rapid training data creation with weak supervision"
    - "csgnet neural shape parser for constructive solid geometry"
    - "an actor critic algorithm for sequence prediction"
    - "label free supervision of neural networks with physics and domain knowledge"
    - "reward augmented maximum likelihood for neural structured prediction"
    - "structured prediction energy networks"
    - "sequence level training with recurrent 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)