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 "mmdf2018 workshop report"):
  Abstract: Driven by the recent advances in smart, miniaturized, and mass produced sensors, networked systems, and high-speed data communication and computing, the ability to collect and process larger volumes of higher veracity real-time data from a variety of modalities is expanding. However, despite research thrusts explored since the late 1990's, to date no standard, generalizable solutions have emerged for effectively integrating and processing multimodal data, and consequently practitioners across a wide variety of disciplines must still follow a trial-and-error process to identify the optimum procedure for each individual application and data sources. A deeper understanding of the utility and capabilities (as well as the shortcomings and challenges) of existing multimodal data fusion methods as a function of data and challenge characteristics has the potential to deliver better data analysis tools across all sectors, therein enabling more efficient and effective automated manufacturing, patient care, infrastructure maintenance, environmental understanding, transportation networks, energy systems, etc. There is therefore an urgent need to identify the underlying patterns that can be used to determine a priori which techniques will be most useful for any specific dataset or application. This next stage of understanding and discovery (i.e., the development of generalized solutions) can only be achieved via a high level cross-disciplinary aggregation of learnings, and this workshop was proposed at an opportune time as many domains have already started exploring use of multimodal data fusion techniques in a wide range of application-specific contexts.

Includes members:
  Member "applications of deep learning and reinforcement learning to biological data":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "3d aggregated faster r cnn for general lesion detection")
  Member "clinical intervention prediction and understanding using deep networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "what went wrong and when instance wise feature importance for time series models")
  Member "benchmark of deep learning models on large healthcare mimic datasets":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "hierarchical target attentive diagnosis prediction in heterogeneous information networks")
  Member "deep learning in neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "π nets deep polynomial neural networks")

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.OH(Other Computer Science)