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: "fmhash deep hashing of in air handwriting for user identification"
  Abstract: Gesture biometrics are gaining popularity with gesture input interface on mobile and Virtual Reality (VR) platforms that lack a keyboard or touchscreen to type a password for user authentication. However, less attention is paid to the gesture-based user identification problem, which essentially requires indexing and searching the gesture motion templates in a large database efficiently. In this paper, we propose FMHash, a user identification framework that can generate a compact binary hash code from a piece of in-air-handwriting of an ID string, which allows fast search in a database of in-air-handwriting templates through a hash table. To demonstrate the effectiveness of the framework, we implemented a prototype and report preliminary results (~98% precision and ~93% recall). More detailed evaluation, comparison, and improvement is working-in-progress. The ability of hashing in-air-handwriting pattern to binary code can be used to achieve convenient sign-in and sign-up with in-air-handwriting gesture ID on future mobile and VR devices.

Connected through hyperedge HE-1 (induced by source paper "fmcode a 3d in the air finger motion based user login framework for gesture interface"):
  with members:
    - "airdraw leveraging smart watch motion sensors for mobile human computer interactions"
    - "handwritten signature verification using hand worn devices"
    - "xception deep learning with depthwise separable convolutions"
    - "forgery resistant touch based authentication on mobile devices"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"
    - "engineering gesture based authentication systems"

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)