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: "deep embedding forest forest based serving with deep embedding features"
  Abstract: Deep Neural Networks (DNN) have demonstrated superior ability to extract high level embedding vectors from low level features. Despite the success, the serving time is still the bottleneck due to expensive run-time computation of multiple layers of dense matrices. GPGPU, FPGA, or ASIC-based serving systems require additional hardware that are not in the mainstream design of most commercial applications. In contrast, tree or forest-based models are widely adopted because of low serving cost, but heavily depend on carefully engineered features. This work proposes a Deep Embedding Forest model that benefits from the best of both worlds. The model consists of a number of embedding layers and a forest/tree layer. The former maps high dimensional (hundreds of thousands to millions) and heterogeneous low-level features to the lower dimensional (thousands) vectors, and the latter ensures fast serving. #R##N#Built on top of a representative DNN model called Deep Crossing, and two forest/tree-based models including XGBoost and LightGBM, a two-step Deep Embedding Forest algorithm is demonstrated to achieve on-par or slightly better performance as compared with the DNN counterpart, with only a fraction of serving time on conventional hardware. After comparing with a joint optimization algorithm called partial fuzzification, also proposed in this paper, it is concluded that the two-step Deep Embedding Forest has achieved near optimal performance. Experiments based on large scale data sets (up to 1 billion samples) from a major sponsored search engine proves the efficacy of the proposed model.

Connected through hyperedge HE-1 (induced by source paper "autoint automatic feature interaction learning via self attentive neural networks"):
  with members:
    - "session based social recommendation via dynamic graph attention networks"
    - "xdeepfm combining explicit and implicit feature interactions for recommender systems"
    - "nais neural attentive item similarity model for recommendation"
    - "neural factorization machines for sparse predictive analytics"
    - "attention is all you need"
    - "deep cross network for ad click predictions"
    - "deepfm a factorization machine based neural network for ctr prediction"
    - "a structured self attentive sentence embedding"

Connected through hyperedge HE-2 (induced by source paper "flen leveraging field for scalable ctr prediction"):
  with members:
    - "a sparse deep factorization machine for efficient ctr prediction"
    - "feature generation by convolutional neural network for click through rate prediction"
    - "operation aware neural networks for user response prediction"
    - "product based neural networks for user response prediction over multi field categorical data"
    - "xdeepfm combining explicit and implicit feature interactions for recommender systems"
    - "field weighted factorization machines for click through rate prediction in display advertising"
    - "pbodl parallel bayesian online deep learning for click through rate prediction in tencent advertising system"
    - "neural factorization machines for sparse predictive analytics"

Connected through hyperedge HE-3 (induced by source paper "deepcf a unified framework of representation learning and matching function learning in recommender system"):
  with members:
    - "outer product based neural collaborative filtering"
    - "neural factorization machines for sparse predictive analytics"
    - "neural collaborative filtering"
    - "fast matrix factorization for online recommendation with implicit feedback"
    - "deepfm a factorization machine based neural network for ctr prediction"
    - "wide deep learning for recommender systems"
    - "vbpr visual bayesian personalized ranking from implicit feedback"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-4 (induced by source paper "exact and consistent interpretation for piecewise linear neural networks a closed form solution"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "smoothgrad removing noise by adding noise"
    - "interpreting deep visual representations via network dissection"
    - "axiomatic attribution for deep networks"
    - "learning important features through propagating activation differences"
    - "nearly tight vc dimension and pseudodimension bounds for piecewise linear neural networks"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "interpretable explanations of black boxes by meaningful perturbation"

Connected through hyperedge HE-5 (induced by source paper "learning from multi view multi way data via structural factorization machines"):
  with members:
    - "neural factorization machines for sparse predictive analytics"
    - "deepmood modeling mobile phone typing dynamics for mood detection"
    - "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-6 (induced by source paper "deep learning in business analytics and operations research models applications and managerial implications"):
  with members:
    - "putting question answering systems into practice transfer learning for efficient domain customization"
    - "sentiment analysis based on rhetorical structure theory learning deep neural networks from discourse trees"
    - "forecasting remaining useful life interpretable deep learning approach via variational bayesian inferences"
    - "similarity encoding for learning with dirty categorical variables"
    - "decision support with text based emotion recognition deep learning for affective computing"
    - "deep learning for affective computing text based emotion recognition in decision support"
    - "decision support from financial disclosures with deep neural networks and transfer learning"
    - "accelerating innovation through analogy mining"

Connected through hyperedge HE-7 (induced by source paper "recurrent binary embedding for gpu enabled exhaustive retrieval from billion scale semantic vectors"):
  with members:
    - "deep learning as a mixed convex combinatorial optimization problem"
    - "billion scale similarity search with gpus"
    - "hashnet deep learning to hash by continuation"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "efficient large scale approximate nearest neighbor search on the gpu"
    - "xnor net imagenet classification using binary convolutional neural networks"
    - "ternary neural networks for resource efficient ai applications"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-8 (induced by source paper "learning cheap and novel flight itineraries"):
  with members:
    - "customer lifetime value prediction using embeddings"
    - "related pins at pinterest the evolution of a real world recommender system"
    - "entity embeddings of categorical variables"
    - "deepcity a feature learning framework for mining location check ins"
    - "deepwalk online learning of social representations"
    - "distributed representations of words and phrases and their compositionality"

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)