human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "exploiting data and human knowledge for predicting wildlife poaching"):
  Abstract: Poaching continues to be a significant threat to the conservation of wildlife and the associated ecosystem. Estimating and predicting where the poachers have committed or would commit crimes is essential to more effective allocation of patrolling resources. The real-world data in this domain is often sparse, noisy and incomplete, consisting of a small number of positive data (poaching signs), a large number of negative data with label uncertainty, and an even larger number of unlabeled data. Fortunately, domain experts such as rangers can provide complementary information about poaching activity patterns. However, this kind of human knowledge has rarely been used in previous approaches.   In this paper, we contribute new solutions to the predictive analysis of poaching patterns by exploiting both very limited data and human knowledge. We propose an approach to elicit quantitative information from domain experts through a questionnaire built upon a clustering-based division of the conservation area. In addition, we propose algorithms that exploit qualitative and quantitative information provided by the domain experts to augment the dataset and improve learning. In collaboration with World Wild Fund for Nature, we show that incorporating human knowledge leads to better predictions in a conservation area in Northeastern China where the charismatic species is Siberian Tiger. The results show the importance of exploiting human knowledge when learning from limited data.

Includes members:
  Member "learning from labeled and unlabeled data an empirical study across techniques and domains":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "performance analysis of semi supervised learning in the small data regime using vaes")
  Member "smote synthetic minority over sampling technique":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "refuge challenge a unified framework for evaluating automated methods for glaucoma assessment from fundus photographs")

Question: Please tell me which class the source paper 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.CY(Computers and Society)