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: "applying deep learning to basketball trajectories"
  Abstract: One of the emerging trends for sports analytics is the growing use of player and ball tracking data. A parallel development is deep learning predictive approaches that use vast quantities of data with less reliance on feature engineering. This paper applies recurrent neural networks in the form of sequence modeling to predict whether a three-point shot is successful. The models are capable of learning the trajectory of a basketball without any knowledge of physics. For comparison, a baseline static machine learning model with a full set of features, such as angle and velocity, in addition to the positional data is also tested. Using a dataset of over 20,000 three pointers from NBA SportVu data, the models based simply on sequential positional data outperform a static feature rich machine learning model in predicting whether a three-point shot is successful. This suggests deep learning models may offer an improvement to traditional feature based machine learning methods for tracking data.

Connected through hyperedge HE-1 (induced by source paper "basketballgan generating basketball play simulation through sketching"):
  with members:
    - "spectral normalization for generative adversarial networks"
    - "began boundary equilibrium generative adversarial networks"
    - "video generation from text"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "improved training of wasserstein gans"
    - "energy based generative adversarial network"
    - "improved techniques for training gans"
    - "image to image translation with conditional adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "a survey of content aware video analysis for sports"):
  with members:
    - "going deeper into action recognition a survey"
    - "a robust and efficient video representation for action recognition"
    - "beyond short snippets deep networks for video classification"
    - "every moment counts dense detailed labeling of actions in complex videos"
    - "action recognition with trajectory pooled deep convolutional descriptors"
    - "translating videos to natural language using deep recurrent neural networks"
    - "reading text in the wild with convolutional neural networks"
    - "two stream convolutional networks for action recognition in videos"

Connected through hyperedge HE-3 (induced by source paper "human pose and path estimation from aerial video using dynamic classifier selection"):
  with members:
    - "soft hardwired attention an lstm framework for human trajectory prediction and abnormal event detection"
    - "convolutional pose machines"
    - "stacked hourglass networks for human pose estimation"
    - "learning a deep model for human action recognition from novel viewpoints"
    - "deepcut joint subset partition and labeling for multi person pose estimation"
    - "how transferable are features in deep neural networks"
    - "modeep a deep learning framework using motion features for human pose estimation"
    - "solving multiclass learning problems via error correcting output codes"

Connected through hyperedge HE-4 (induced by source paper "deep learning for iot big data and streaming analytics a survey"):
  with members:
    - "on accurate and reliable anomaly detection for gas turbine combustors a deep learning approach"
    - "enabling cognitive smart cities using big data and machine learning approaches and challenges"
    - "semisupervised deep reinforcement learning in support of iot and smart city services"
    - "in datacenter performance analysis of a tensor processing unit"
    - "an analysis of deep neural network models for practical applications"
    - "approxdbn approximate computing for discriminative deep belief networks"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "applying neural networks in optical communication systems possible pitfalls"

Connected through hyperedge HE-5 (induced by source paper "autonomous driving challenge to infer the property of a dynamic object based on its motion pattern using recurrent neural network"):
  with members:
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "long term recurrent convolutional networks for visual recognition and description"

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.NE(Neural and Evolutionary Computing)