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: "on clustering time series using euclidean distance and pearson correlation"
  Abstract: For time series comparisons, it has often been observed that z-score normalized Euclidean distances far outperform the unnormalized variant. In this paper we show that a z-score normalized, squared Euclidean Distance is, in fact, equal to a distance based on Pearson Correlation. This has profound impact on many distance-based classification or clustering methods. In addition to this theoretically sound result we also show that the often used k-Means algorithm formally needs a mod ification to keep the interpretation as Pearson correlation strictly valid. Experimental results demonstrate that in many cases the standard k-Means algorithm generally produces the same results.

Connected through hyperedge HE-1 (induced by source paper "unsupervised learning of depth and deep representation for visual odometry from monocular videos in a metric space"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "codeslam learning a compact optimisable representation for dense visual slam"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "pixel wise attentional gating for parsimonious pixel labeling"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "unsupervised learning of depth and ego motion from video"
    - "learning depth from monocular videos using direct methods"
    - "demon depth and motion network for learning monocular stereo"

Connected through hyperedge HE-2 (induced by source paper "dcso dynamic combination of detector scores for outlier ensembles"):
  with members:
    - "xgbod improving supervised outlier detection with unsupervised representation learning"
    - "sequential ensemble learning for outlier detection a bias variance perspective"
    - "local subspace based outlier detection using global neighbourhoods"
    - "learning representations for outlier detection on a budget"
    - "less is more building selective anomaly ensembles"

Connected through hyperedge HE-3 (induced by source paper "smerc social media event response clustering using textual and temporal information"):
  with members:
    - "pachinko prediction a bayesian method for event prediction from social media data"
    - "real time detection of content polluters in partially observable twitter networks"
    - "tweet2vec character based distributed representations for social media"
    - "a data driven model for influenza transmission incorporating media effects"
    - "event detection tracking and visualization in twitter a mention anomaly based approach"
    - "seismic a self exciting point process model for predicting tweet popularity"
    - "efficient estimation of word representations in vector space"
    - "human as real time sensors of social and physical events a case study of twitter and sports games"

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)