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: "deepmood modeling mobile phone typing dynamics for mood detection"
  Abstract: The increasing use of electronic forms of communication presents new opportunities in the study of mental health, including the ability to investigate the manifestations of psychiatric diseases unobtrusively and in the setting of patients' daily lives. A pilot study to explore the possible connections between bipolar affective disorder and mobile phone usage was conducted. In this study, participants were provided a mobile phone to use as their primary phone. This phone was loaded with a custom keyboard that collected metadata consisting of keypress entry time and accelerometer movement. Individual character data with the exceptions of the backspace key and space bar were not collected due to privacy concerns. We propose an end-to-end deep architecture based on late fusion, named DeepMood, to model the multi-view metadata for the prediction of mood scores. Experimental results show that 90.31% prediction accuracy on the depression score can be achieved based on session-level mobile phone typing dynamics which is typically less than one minute. It demonstrates the feasibility of using mobile phone metadata to infer mood disturbance and severity.

Connected through hyperedge HE-1 (induced by source paper "deep learning towards mobile applications"):
  with members:
    - "privynet a flexible framework for privacy preserving deep neural network training"
    - "not just privacy improving performance of private deep learning in mobile cloud"
    - "sequential keystroke behavioral biometrics for mobile user identification via multi view deep learning"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "learning differentially private recurrent language models"
    - "modeling source syntax for neural machine translation"
    - "distributed deep neural networks over the cloud the edge and end devices"
    - "c ir cnn accelerating and compressing deep neural networks using block circulant weight matrices"

Connected through hyperedge HE-2 (induced by source paper "product based neural networks for user response prediction over multi field categorical data"):
  with members:
    - "deep learning based recommender system a survey and new perspectives"
    - "deep sets"
    - "self normalizing neural networks"
    - "neural factorization machines for sparse predictive analytics"
    - "neural collaborative filtering"
    - "failures of gradient based deep learning"
    - "deepfm a factorization machine based neural network for ctr prediction"
    - "attentional factorization machines learning the weight of feature interactions via attention networks"

Connected through hyperedge HE-3 (induced by source paper "not just privacy improving performance of private deep learning in mobile cloud"):
  with members:
    - "privynet a flexible framework for privacy preserving deep neural network training"
    - "sequential keystroke behavioral biometrics for mobile user identification via multi view deep learning"
    - "adversarial dropout for supervised and semi supervised learning"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "modeling source syntax for neural machine translation"
    - "distributed deep neural networks over the cloud the edge and end devices"
    - "c ir cnn accelerating and compressing deep neural networks using block circulant weight matrices"
    - "a hybrid deep learning architecture for privacy preserving mobile analytics"

Connected through hyperedge HE-4 (induced by source paper "learning from multi view multi way data via structural factorization machines"):
  with members:
    - "neural factorization machines for sparse predictive analytics"
    - "deep embedding forest forest based serving with deep embedding features"
    - "deep cross network for ad click predictions"
    - "a broad learning approach for context aware mobile application recommendation"
    - "joint deep modeling of users and items using reviews for recommendation"
    - "deepfm a factorization machine based neural network for ctr prediction"
    - "attentional factorization machines learning the weight of feature interactions via attention networks"
    - "wide deep learning for recommender systems"

Connected through hyperedge HE-5 (induced by source paper "private model compression via knowledge distillation"):
  with members:
    - "generating differentially private datasets using gans"
    - "not just privacy improving performance of private deep learning in mobile cloud"
    - "adversarial dropout for supervised and semi supervised learning"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "a hybrid deep learning architecture for privacy preserving mobile analytics"
    - "deep models under the gan information leakage from collaborative deep learning"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "fitnets hints for thin deep nets"

Connected through hyperedge HE-6 (induced by source paper "mv rnn a multi view recurrent neural network for sequential recommendation"):
  with members:
    - "a survey of multi view representation learning"
    - "multi behavioral sequential prediction with recurrent log bilinear model"
    - "inferring networks of substitutable and complementary products"
    - "session based recommendations with recurrent neural networks"
    - "vbpr visual bayesian personalized ranking from implicit feedback"
    - "image based recommendations on styles and substitutes"
    - "gated feedback recurrent neural networks"
    - "going deeper with convolutions"

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.HC(Human-Computer Interaction)