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: "an entity resolution approach to isolate instances of human trafficking online"
  Abstract: Human trafficking is a challenging law enforcement problem, with a large amount of such activity taking place online. Given the large, heterogeneous and noisy structure of this data, the problem becomes even more challenging. In this paper we propose and entity resolution pipeline using a notion of proxy labels, in order to extract clusters from this data with prior history of human trafficking activity. We apply this pipeline to 5M records from backpage.com and report on the performance of this approach, challenges in terms of scalability, and some significant domain specific characteristics of our resolved entities.

Connected through hyperedge HE-1 (induced by source paper "always lurking understanding and mitigating bias in online human trafficking detection"):
  with members:
    - "men also like shopping reducing gender bias amplification using corpus level constraints"
    - "semantics derived automatically from language corpora contain human like biases"
    - "enriching word vectors with subword information"
    - "character level convolutional networks for text classification"
    - "parallel correlation clustering on big graphs"
    - "incremental knowledge base construction using deepdive"
    - "renyi divergence and kullback leibler divergence"

Connected through hyperedge HE-2 (induced by source paper "combating human trafficking with deep multimodal models"):
  with members:
    - "image captioning with semantic attention"
    - "mosi multimodal corpus of sentiment intensity and subjectivity analysis in online opinion videos"
    - "vqa visual question answering"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"
    - "long term recurrent convolutional networks for visual recognition and description"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-3 (induced by source paper "semi supervised learning for detecting human trafficking"):
  with members:
    - "a non parametric learning approach to identify online human trafficking"
    - "identifying community structures in dynamic networks"
    - "signed link analysis in social media networks"
    - "supervised random walks predicting and recommending links in social 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.SI(Social and Information Networks)