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: "identifying civilians killed by police with distantly supervised entity event extraction"
  Abstract: We propose a new, socially-impactful task for natural language processing: from a news corpus, extract names of persons who have been killed by police. We present a newly collected police fatality corpus, which we release publicly, and present a model to solve this problem that uses EM-based distant supervision with logistic regression and convolutional neural network classifiers. Our model outperforms two off-the-shelf event extractor systems, and it can suggest candidate victim names in some cases faster than one of the major manually-collected police fatality databases.

Connected through hyperedge HE-1 (induced by source paper "geolocating political events in text"):
  with members:
    - "lost in space geolocation in event data"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "encoding sentences with graph convolutional networks for semantic role labeling"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-2 (induced by source paper "deep probabilistic logic a unifying framework for indirect supervision"):
  with members:
    - "heterogeneous supervision for relation extraction a representation learning approach"
    - "learning the structure of generative models without labeled data"
    - "improving coreference resolution by learning entity level distributed representations"
    - "distant supervision for relation extraction beyond the sentence boundary"
    - "leveraging deep neural networks and knowledge graphs for entity disambiguation"
    - "teaching machines to read and comprehend"
    - "semi supervised learning with deep generative models"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-3 (induced by source paper "multimodal social media analysis for gang violence prevention"):
  with members:
    - "using natural language processing and qualitative analysis to intervene in gang violence a collaboration between social work researchers and data scientists"
    - "r fcn object detection via region based fully convolutional networks"
    - "fast r cnn"
    - "rethinking the inception architecture for computer vision"
    - "convolutional neural networks for sentence classification"
    - "cnn features off the shelf an astounding baseline for recognition"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-4 (induced by source paper "jointly multiple events extraction via attention based graph information aggregation"):
  with members:
    - "encoding sentences with graph convolutional networks for semantic role labeling"
    - "language modeling with gated convolutional networks"
    - "joint extraction of events and entities within a document context"
    - "training very deep networks"
    - "adadelta an adaptive learning rate method"

Connected through hyperedge HE-5 (induced by source paper "detecting gang involved escalation on social media using context"):
  with members:
    - "finding street gang members on twitter"
    - "demographic dialectal variation in social media a case study of african american english"
    - "efficient estimation of word representations in vector space"
    - "natural language processing almost from scratch"

Connected through hyperedge HE-6 (induced by source paper "who is killed by police introducing supervised attention for hierarchical lstms"):
  with members:
    - "neural machine translation with supervised attention"
    - "neural summarization by extracting sentences and words"
    - "supervised attentions for neural machine translation"
    - "neural machine translation by jointly learning to align and translate"

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)