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: "entity embedding based anomaly detection for heterogeneous categorical events"
  Abstract: Anomaly detection plays an important role in modern data-driven security applications, such as detecting suspicious access to a socket from a process. In many cases, such events can be described as a collection of categorical values that are considered as entities of different types, which we call heterogeneous categorical events. Due to the lack of intrinsic distance measures among entities, and the exponentially large event space, most existing work relies heavily on heuristics to calculate abnormal scores for events. Different from previous work, we propose a principled and unified probabilistic model APE (Anomaly detection via Probabilistic pairwise interaction and Entity embedding) that directly models the likelihood of events. In this model, we embed entities into a common latent space using their observed co-occurrence in different events. More specifically, we first model the compatibility of each pair of entities according to their embeddings. Then we utilize the weighted pairwise interactions of different entity types to define the event probability. Using Noise-Contrastive Estimation with "context-dependent" noise distribution, our model can be learned efficiently regardless of the large event space. Experimental results on real enterprise surveillance data show that our methods can accurately detect abnormal events compared to other state-of-the-art abnormal detection techniques.

Connected through hyperedge HE-1 (induced by source paper "supertml two dimensional word embedding for the precognition on structured tabular data"):
  with members:
    - "squared english word a method of generating glyph to use super characters for sentiment analysis"
    - "neural feature learning from relational database"
    - "deep contextualized word representations"
    - "ultra power efficient cnn domain specific accelerator with 9 3tops watt for mobile and embedded applications"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "super characters a conversion from sentiment classification to image classification"
    - "catboost unbiased boosting with categorical features"
    - "progressive neural architecture search"

Connected through hyperedge HE-2 (induced by source paper "task guided pair embedding in heterogeneous network"):
  with members:
    - "link prediction based on graph neural networks"
    - "metagraph2vec complex semantic path augmented heterogeneous network embedding"
    - "easing embedding learning by comprehensive transcription of heterogeneous information networks"
    - "aspem embedding learning by aspects in heterogeneous information networks"
    - "capturing edge attributes via network embedding"
    - "an attention based collaboration framework for multi view network representation learning"
    - "advances in pre training distributed word representations"
    - "heterogeneous information network embedding for recommendation"

Connected through hyperedge HE-3 (induced by source paper "sequence embeddings help to identify fraudulent cases in healthcare insurance"):
  with members:
    - "baseline needs more love on simple word embedding based models and associated pooling mechanisms"
    - "anonymous walk embeddings"
    - "on the dimensionality of word embedding"
    - "representation learning on graphs methods and applications"
    - "practical black box attacks against machine learning"
    - "xgboost a scalable tree boosting system"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "imbalanced learn a python toolbox to tackle the curse of imbalanced datasets in machine learning"

Connected through hyperedge HE-4 (induced by source paper "heterogeneous graph matching networks"):
  with members:
    - "adversarial defense framework for graph neural network"
    - "medgcn graph convolutional networks for multiple medical tasks"
    - "a comprehensive survey on graph neural networks"
    - "hierarchical graph representation learning with differentiable pooling"
    - "modeling polypharmacy side effects with graph convolutional networks"
    - "fastgcn fast learning with graph convolutional networks via importance sampling"
    - "neural message passing for quantum chemistry"
    - "burstiness of intrusion detection process empirical evidence and a modeling approach"

Connected through hyperedge HE-5 (induced by source paper "saec similarity aware embedding compression in recommendation systems"):
  with members:
    - "amc automl for model compression and acceleration on mobile devices"
    - "deep k means re training and parameter sharing with harder cluster assignments for compressing deep convolutions"
    - "learning k way d dimensional discrete code for compact embedding representations"
    - "compressing word embeddings via deep compositional code learning"
    - "learning structured sparsity in deep neural networks"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "learning both weights and connections for efficient neural networks"
    - "compressing neural networks with the hashing trick"

Connected through hyperedge HE-6 (induced by source paper "who are the phishers phishing scam detection on ethereum via network embedding"):
  with members:
    - "the art of the scam demystifying honeypots in ethereum smart contracts"
    - "the ico phenomenon and its relationships with ethereum smart contract environment"
    - "a semantic framework for the security analysis of ethereum smart contracts"
    - "a survey on security and privacy issues of bitcoin"
    - "dissecting ponzi schemes on ethereum identification analysis and impact"
    - "node2vec scalable feature learning for networks"
    - "deepwalk online learning of social representations"
    - "graph based anomaly detection and description a survey"

Connected through hyperedge HE-7 (induced by source paper "learning k way d dimensional discrete code for compact embedding representations"):
  with members:
    - "quantization and training of neural networks for efficient integer arithmetic only inference"
    - "fasttext zip compressing text classification models"
    - "compressing word embeddings via deep compositional code learning"
    - "semi supervised classification with graph convolutional networks"
    - "the concrete distribution a continuous relaxation of discrete random variables"
    - "lightrnn memory and computation efficient recurrent neural networks"
    - "bag of tricks for efficient text classification"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"

Connected through hyperedge HE-8 (induced by source paper "on sampling strategies for neural network based collaborative filtering"):
  with members:
    - "joint text embedding for personalized content based recommendation"
    - "leveraging visual question answering for image caption ranking"
    - "task guided and path augmented heterogeneous network embedding for author identification"
    - "a neural autoregressive approach to collaborative filtering"
    - "parallelizing word2vec in shared and distributed memory"
    - "character level convolutional networks for text classification"
    - "line large scale information network embedding"
    - "adam a method for stochastic optimization"

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)