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: "toeplitz inverse covariance based clustering of multivariate time series data"
  Abstract: Subsequence clustering of multivariate time series is a useful tool for discovering repeated patterns in temporal data. Once these patterns have been discovered, seemingly complicated datasets can be interpreted as a temporal sequence of only a small number of states, or clusters. For example, raw sensor data from a fitness-tracking application can be expressed as a timeline of a select few actions (i.e., walking, sitting, running). However, discovering these patterns is challenging because it requires simultaneous segmentation and clustering of the time series. Furthermore, interpreting the resulting clusters is difficult, especially when the data is high-dimensional. Here we propose a new method of model-based clustering, which we call Toeplitz Inverse Covariance-based Clustering (TICC). Each cluster in the TICC method is defined by a correlation network, or Markov random field (MRF), characterizing the interdependencies between different observations in a typical subsequence of that cluster. Based on this graphical representation, TICC simultaneously segments and clusters the time series data. We solve the TICC problem through alternating minimization, using a variation of the expectation maximization (EM) algorithm. We derive closed-form solutions to efficiently solve the two resulting subproblems in a scalable way, through dynamic programming and the alternating direction method of multipliers (ADMM), respectively. We validate our approach by comparing TICC to several state-of-the-art baselines in a series of synthetic experiments, and we then demonstrate on an automobile sensor dataset how TICC can be used to learn interpretable clusters in real-world scenarios.

Connected through hyperedge HE-1 (induced by source paper "indirect adversarial attacks via poisoning neighbors for graph convolutional networks"):
  with members:
    - "robust audio adversarial example for a physical attack"
    - "adversarial attacks on graph neural networks via meta learning"
    - "adversarial attack on graph structured data"
    - "graph convolutional neural networks for web scale recommender systems"
    - "lipschitz margin training scalable certification of perturbation invariance for deep neural networks"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "robust physical world attacks on deep learning models"
    - "node2vec scalable feature learning for networks"

Connected through hyperedge HE-2 (induced by source paper "scalable hybrid hmm with gaussian process emission for sequential time series data clustering"):
  with members:
    - "neural non stationary spectral kernel"
    - "the ucr time series archive"
    - "unsupervised neural hidden markov models"

Connected through hyperedge HE-3 (induced by source paper "summertime variable length time seriessummarization with applications to physicalactivity analysis"):
  with members:
    - "multivariate lstm fcns for time series classification"

Connected through hyperedge HE-4 (induced by source paper "modeling combinatorial evolution in time series prediction"):
  with members:
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "unsupervised anomaly detection via variational auto encoder for seasonal kpis in web applications"
    - "graph networks as learnable physics engines for inference and control"
    - "multilevel wavelet decomposition network for interpretable time series analysis"
    - "high order graph convolutional recurrent neural network a deep learning framework for network scale traffic learning and forecasting"
    - "relational inductive bias for physical construction in humans and machines"
    - "action schema networks generalised policies with deep learning"
    - "neural message passing for quantum chemistry"

Connected through hyperedge HE-5 (induced by source paper "discovering patterns of online popularity from time series"):
  with members:
    - "expecting to be hip hawkes intensity processes for social media popularity"
    - "online human bot interactions detection estimation and characterization"
    - "botornot a system to evaluate social bots"
    - "on the dynamics of social media popularity a youtube case study"
    - "is the sample good enough comparing data from twitter s streaming api with twitter s firehose"
    - "dynamical classes of collective attention in twitter"
    - "information contagion an empirical study of the spread of news on digg and twitter social networks"
    - "novelty and collective attention"

Connected through hyperedge HE-6 (induced by source paper "learning behavioral representations from wearable sensors"):
  with members:
    - "characterizing activity on the deep and dark web"
    - "discovering hidden structure in high dimensional human behavioral data via tensor factorization"
    - "tensor embedding a supervised framework for human behavioral data mining and prediction"
    - "random warping series a random features method for time series embedding"
    - "non negative tensor factorization for human behavioral pattern mining in online games"
    - "modeling individual cyclic variation in human behavior"
    - "human gender classification a review"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-7 (induced by source paper "toeplitz inverse covariance based robust speaker clustering for naturalistic audio streams"):
  with members:
    - "robust speaker clustering using mixtures of von mises fisher distributions for naturalistic audio streams"
    - "robust feature clustering for unsupervised speech activity detection"
    - "using speech technology for quantifying behavioral characteristics in peer led team learning sessions"
    - "a robust diarization system for measuring dominance in peer led team learning groups"
    - "a speaker diarization system for studying peer led team learning groups"

Connected through hyperedge HE-8 (induced by source paper "clustering time series by a novel slope based similarity measure considering particle swarm optimization"):
  with members:
    - "clustering time series and the surprising robustness of hmms"
    - "an empirical evaluation of similarity measures for time series classification"
    - "clustering time series data stream a literature survey"
    - "time warp edit distance with stiffness adjustment for time series matching"

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)