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: "learning sentence internal temporal relations"
  Abstract: In this paper we propose a data intensive approach for inferring sentence-internal temporal relations. Temporal inference is relevant for practical NLP applications which either extract or synthesize temporal information (e.g., summarisation, question answering). Our method bypasses the need for manual coding by exploiting the presence of markers like after, which overtly signal a temporal relation. We first show that models trained on main and subordinate clauses connected with a temporal marker achieve good performance on a pseudo-disambiguation task simulating temporal inference (during testing the temporal marker is treated as unseen and the models must select the right marker from a set of possible candidates). Secondly, we assess whether the proposed approach holds promise for the semi-automatic creation of temporal annotations. Specifically, we use a model trained on noisy and approximate data (i.e., main and subordinate clauses) to predict intra-sentential relations present in TimeBank, a corpus annotated rich temporal information. Our experiments compare and contrast several probabilistic models differing in their feature space, linguistic assumptions and data requirements. We evaluate performance against gold standard corpora and also against human subjects.

Connected through hyperedge HE-1 (induced by source paper "compositional temporal visual grounding of natural language event descriptions"):
  with members:
    - "wman weakly supervised moment alignment network for text based video segment retrieval"
    - "man moment alignment network for natural language moment retrieval via iterative graph adjustment"
    - "mac mining activity concepts for language based temporal localization"
    - "w talc weakly supervised temporal activity localization and classification"
    - "untrimmednets for weakly supervised action recognition and detection"
    - "weakly supervised action localization by sparse temporal pooling network"
    - "tall temporal activity localization via language query"
    - "localizing moments in video with natural language"

Connected through hyperedge HE-2 (induced by source paper "sort story sorting jumbled images and captions into stories"):
  with members:
    - "learning visual storylines with skipping recurrent neural networks"
    - "exploring models and data for image question answering"
    - "vqa visual question answering"
    - "skip thought vectors"
    - "towards ai complete question answering a set of prerequisite toy tasks"
    - "learning temporal embeddings for complex video analysis"
    - "aligning books and movies towards story like visual explanations by watching movies and reading books"
    - "order embeddings of images and language"

Connected through hyperedge HE-3 (induced by source paper "automated prediction of temporal relations"):
  with members:
    - "aggressive actions and anger detection from multiple modalities using kinect"
    - "augmenting supervised emotion recognition with rule based decision model"
    - "xml entity architecture for efficient software integration"
    - "embracing agile methodology during devops developer internship program"
    - "affect intensity estimation using multiple modalities"
    - "learning semantic script knowledge with event embeddings"

Connected through hyperedge HE-4 (induced by source paper "a corpus based study of temporal signals"):
  with members:
    - "analysing temporally annotated corpora with cavat"
    - "using signals to improve automatic classification of temporal relations"
    - "enhancing qa systems with complex temporal question processing capabilities"

Connected through hyperedge HE-5 (induced by source paper "learning to predict from textual data"):
  with members:
    - "a survey of paraphrasing and textual entailment methods"
    - "expressing implicit semantic relations without supervision"

Connected through hyperedge HE-6 (induced by source paper "towards unsupervised learning of temporal relations between events"):
  with members:
    - "usfd2 annotating temporal expresions and tlinks for tempeval 2"
    - "more accurate tests for the statistical significance of result differences"

Connected through hyperedge HE-7 (induced by source paper "enhancing qa systems with complex temporal question processing capabilities"):
  with members:
    - "inferring strategies for sentence ordering in multidocument news summarization"
    - "computational approach to anaphora resolution in spanish dialogues"

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)