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 "modeling the intensity function of point process via recurrent neural networks"):
  Abstract: Event sequence, asynchronously generated with random timestamp, is ubiquitous among applications. The precise and arbitrary timestamp can carry important clues about the underlying dynamics, and has lent the event data fundamentally different from the time-series whereby series is indexed with fixed and equal time interval. One expressive mathematical tool for modeling event is point process. The intensity functions of many point processes involve two components: the background and the effect by the history. Due to its inherent spontaneousness, the background can be treated as a time series while the other need to handle the history events. In this paper, we model the background by a Recurrent Neural Network (RNN) with its units aligned with time series indexes while the history effect is modeled by another RNN whose units are aligned with asynchronous events to capture the long-range dynamics. The whole model with event type and timestamp prediction output layers can be trained end-to-end. Our approach takes an RNN perspective to point process, and models its background and history effect. For utility, our method allows a black-box treatment for modeling the intensity which is often a pre-defined parametric form in point processes. Meanwhile end-to-end training opens the venue for reusing existing rich techniques in deep network for point process modeling. We apply our model to the predictive maintenance problem using a log dataset by more than 1000 ATMs from a global bank headquartered in North America.

Includes members:
  Member "predicting clinical events by combining static and dynamic information using recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "raim recurrent attentive and intensive model of multimodal patient monitoring data")
  Member "recurrent neural networks for multivariate time series with missing values":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "highres net recursive fusion for multi frame super resolution of satellite imagery")
  Member "recurrent neural networks for driver activity anticipation via sensory fusion architecture":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "sensor fusion gated recurrent fusion to learn driving behavior from temporal multimodal data")
  Member "equilibrated adaptive learning rates for non convex optimization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deam adaptive momentum with discriminative weight for stochastic optimization")
  Member "doctor ai predicting clinical events via recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "secnlp a survey of embeddings in clinical natural language processing")
  Member "scheduled sampling for sequence prediction with recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "videoflow a conditional flow based model for stochastic video generation")
  Member "draw a recurrent neural network for image generation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "twostreamvan improving motion modeling in video generation")
  Member "rmsprop and equilibrated adaptive learning rates for non convex optimization":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "domain independent dominance of adaptive methods")

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.LG(Machine Learning)