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: "a simple baseline for travel time estimation using large scale trip data"
  Abstract: The increased availability of large-scale trajectory data around the world provides rich information for the study of urban dynamics. For example, New York City Taxi Limousine Commission regularly releases source-destination information about trips in the taxis they regulate. Taxi data provide information about traffic patterns, and thus enable the study of urban flow -- what will traffic between two locations look like at a certain date and time in the future? Existing big data methods try to outdo each other in terms of complexity and algorithmic sophistication. In the spirit of "big data beats algorithms", we present a very simple baseline which outperforms state-of-the-art approaches, including Bing Maps and Baidu Maps (whose APIs permit large scale experimentation). Such a travel time estimation baseline has several important uses, such as navigation (fast travel time estimates can serve as approximate heuristics for A search variants for path finding) and trip planning (which uses operating hours for popular destinations along with travel time estimates to create an itinerary).

Connected through hyperedge HE-1 (induced by source paper "mat a simple yet strong baseline for identifying self admitted technical debt"):
  with members:
    - "towards good practices on building effective cnn baseline model for person re identification"
    - "simple baselines for human pose estimation and tracking"
    - "the effectiveness of instance normalization a strong baseline for single image dehazing"
    - "domain stylization a strong simple baseline for synthetic to real image domain adaptation"
    - "500 times faster than deep learning a case study exploring faster methods for text mining stackoverflow"
    - "show ask attend and answer a strong baseline for visual question answering"
    - "bellwethers a baseline method for transfer learning"
    - "easy over hard a case study on deep learning"

Connected through hyperedge HE-2 (induced by source paper "deepist deep image based spatio temporal network for travel time estimation"):
  with members:
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "deeptravel a neural network based travel time estimation model with auxiliary supervision"
    - "lstm a search space odyssey"
    - "deep sentence embedding using long short term memory networks analysis and application to information retrieval"
    - "unsupervised learning of video representations using lstms"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"
    - "convolutional neural networks for sentence classification"

Connected through hyperedge HE-3 (induced by source paper "to each route its own eta a generative modeling framework for eta prediction"):
  with members:
    - "pixel recurrent neural networks"
    - "an overview of gradient descent optimization algorithms"
    - "travel time estimation using floating car data"

Connected through hyperedge HE-4 (induced by source paper "optimizing taxi carpool policies via reinforcement learning and spatio temporal mining"):
  with members:
    - "learning deep networks from noisy labels with dropout regularization"
    - "deep reinforcement learning with double q learning"
    - "playing atari with deep reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "detecting outliers in data with correlated measures"):
  with members:
    - "robust contextual outlier detection where context meets sparsity"

Connected through hyperedge HE-6 (induced by source paper "rdeepsense reliable deep mobile computing models with uncertainty estimations"):
  with members:
    - "ensembles of deep lstm learners for activity recognition using wearables"
    - "deepiot compressing deep neural network structures for sensing systems with a compressor critic framework"
    - "deepsense a unified deep learning framework for time series mobile sensing data processing"
    - "the mythos of model interpretability"
    - "predicting daily activities from egocentric images using deep learning"

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.LG(Machine Learning)