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 "efficient crowd exploration of large networks the case of causal attribution"):
  Abstract: Accurately and efficiently crowdsourcing complex, open-ended tasks can be difficult, as crowd participants tend to favor short, repetitive "microtasks". We study the crowdsourcing of large networks where the crowd provides the network topology via microtasks. Crowds can explore many types of social and information networks, but we focus on the network of causal attributions, an important network that signifies cause-and-effect relationships. We conduct experiments on Amazon Mechanical Turk (AMT) testing how workers can propose and validate individual causal relationships and introduce a method for independent crowd workers to explore large networks. The core of the method, Iterative Pathway Refinement, is a theoretically-principled mechanism for efficient exploration via microtasks. We evaluate the method using synthetic networks and apply it on AMT to extract a large-scale causal attribution network. Worker interactions reveal important characteristics of causal perception and the generated network data can help improve our understanding of causality and causal inference.

Includes members:
  Member "crowd ideation of supervised learning problems":
    also connected through other hyperedges:
      (none)
  Member "reply supply efficient crowdsourcing when workers do more than answer questions":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "accurate inference of crowdsourcing properties when using efficient allocation strategies")
  Member "crowdsourcing predictors of residential electric energy usage":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "accurate inference of crowdsourcing properties when using efficient allocation strategies")
  Member "distinguishing cause from effect using observational data methods and benchmarks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "causal discovery from incomplete data a deep learning approach")
  Member "crowdsourcing predictors of behavioral outcomes":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "accurate inference of crowdsourcing properties when using efficient allocation strategies")
  Member "search in power law networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a general centrality framework based on node navigability")

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.HC(Human-Computer Interaction)