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: "authenticating users through their arm movement patterns"
  Abstract: In this paper, we propose four continuous authentication designs by using the characteristics of arm movements while individuals walk. The first design uses acceleration of arms captured by a smartwatch's accelerometer sensor, the second design uses the rotation of arms captured by a smartwatch's gyroscope sensor, third uses the fusion of both acceleration and rotation at the feature-level and fourth uses the fusion at score-level. Each of these designs is implemented by using four classifiers, namely, k nearest neighbors (k-NN) with Euclidean distance, Logistic Regression, Multilayer Perceptrons, and Random Forest resulting in a total of sixteen authentication mechanisms. These authentication mechanisms are tested under three different environments, namely an intra-session, inter-session on a dataset of 40 users and an inter-phase on a dataset of 12 users. The sessions of data collection were separated by at least ten minutes, whereas the phases of data collection were separated by at least three months. Under the intra-session environment, all of the twelve authentication mechanisms achieve a mean dynamic false accept rate (DFAR) of 0% and dynamic false reject rate (DFRR) of 0%. For the inter-session environment, feature level fusion-based design with classifier k-NN achieves the best error rates that are a mean DFAR of 2.2% and DFRR of 4.2%. The DFAR and DFRR increased from 5.68% and 4.23% to 15.03% and 14.62% respectively when feature level fusion-based design with classifier k-NN was tested under the inter-phase environment on a dataset of 12 users.

Connected through hyperedge HE-1 (induced by source paper "identification of internal faults in indirect symmetrical phase shift transformers using ensemble learning"):
  with members:
    - "continuous user authentication via unlabeled phone movement patterns"
    - "continuous authentication using one class classifiers and their fusion"
    - "feature based time series analysis"
    - "distributed and parallel time series feature extraction for industrial big data applications"

Connected through hyperedge HE-2 (induced by source paper "tap based user authentication for smartwatches"):
  with members:
    - "illusionpin shoulder surfing resistant authentication using hybrid images"
    - "walk unlock zero interaction authentication protected with multi modal gait biometrics"

Connected through hyperedge HE-3 (induced by source paper "continuous user authentication via unlabeled phone movement patterns"):
  with members:
    - "active user authentication for smartphones a challenge data set and benchmark results"
    - "path person authentication using trace histories"
    - "hmog new behavioral biometric features for continuous authentication of smartphone users"

Connected through hyperedge HE-4 (induced by source paper "continuous authentication using one class classifiers and their fusion"):
  with members:
    - "touchalytics on the applicability of touchscreen input as a behavioral biometric for continuous authentication"

Connected through hyperedge HE-5 (induced by source paper "handwritten signature verification using hand worn devices"):
  with members:
    - "offline handwritten signature verification literature review"
    - "deep spying spying using smartwatch and deep learning"

Connected through hyperedge HE-6 (induced by source paper "curie a method for protecting svm classifier from poisoning attack"):
  with members:
    - "poisoning attacks against support vector machines"

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.CV(Computer Vision and Pattern Recognition)