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: "exploiting partially annotated data for temporal relation extraction"
  Abstract: Annotating temporal relations (TempRel) between events described in natural language is known to be labor intensive, partly because the total number of TempRels is quadratic in the number of events. As a result, only a small number of documents are typically annotated, limiting the coverage of various lexical/semantic phenomena. In order to improve existing approaches, one possibility is to make use of the readily available, partially annotated data (P as in partial) that cover more documents. However, missing annotations in P are known to hurt, rather than help, existing systems. This work is a case study in exploring various usages of P for TempRel extraction. Results show that despite missing annotations, P is still a useful supervision signal for this task within a constrained bootstrapping learning framework. The system described in this system is publicly available.

Connected through hyperedge HE-1 (induced by source paper "partial or complete that s the question"):
  with members:
    - "joint reasoning for temporal and causal relations"
    - "cogcomptime a tool for understanding time in natural language text"
    - "understanding autoencoders with information theoretic concepts"
    - "a multi axis annotation scheme for event temporal relations"
    - "active learning with partial feedback"
    - "structured set matching networks for one shot part labeling"
    - "opening the black box of deep neural networks via information"
    - "crowdsourcing question answer meaning representations"

Connected through hyperedge HE-2 (induced by source paper "named entity recognition with partially annotated training data"):
  with members:
    - "training a neural network in a low resource setting on automatically annotated noisy data"
    - "snorkel rapid training data creation with weak supervision"
    - "swellshark a generative model for biomedical named entity recognition without labeled data"
    - "fidelity weighted learning"
    - "neural architectures for named entity recognition"
    - "introduction to the conll 2003 shared task language independent named entity recognition"

Connected through hyperedge HE-3 (induced by source paper "a multi axis annotation scheme for event temporal relations"):
  with members:
    - "a structured learning approach to temporal relation extraction"
    - "improving temporal relation extraction with a globally acquired statistical resource"

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)