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: "microexpnet an extremely small and fast model for expression recognition from frontal face images"
  Abstract: This paper is aimed at creating extremely small and fast convolutional neural networks (CNN) for the problem of facial expression recognition (FER) from frontal face images. To this end, we employed the popular knowledge distillation (KD) method and identified two major shortcomings with its use: 1) a fine-grained grid search is needed for tuning the temperature hyperparameter and 2) to find the optimal size-accuracy balance, one needs to search for the final network size (i.e. the compression rate). On the other hand, KD is proved to be useful for model compression for the FER problem, and we discovered that its effects gets more and more significant with the decreasing model size. In addition, during the search for a network architecture, we hypothesized that translation invariance achieved using max-pooling layers would not be useful for the FER problem as the expressions are sensitive to small, pixel-wise changes around the eye and the mouth. However, we have found an intriguing improvement on generalization when max-pooling is used. Experiments are made on two widely-used FER datasets, CK+ and Oulu-CASIA. Our smallest model (MicroExpNet), obtained using knowledge distillation, is less than 1MB in size and works at 1851 frames per second on an Intel i7 CPU. Despite being less accurate than the state-of-the-art, MicroExpNet still provides significant insights on the creation of a micro architecture for the FER problem.

Connected through hyperedge HE-1 (induced by source paper "rescue a framework for real time feedback on behavioral cues using multimodal anomaly detection"):
  with members:
    - "convolutional pose machines"

Connected through hyperedge HE-2 (induced by source paper "dqn tamer human in the loop reinforcement learning with intractable feedback"):
  with members:
    - "rudder return decomposition for delayed rewards"
    - "interactive learning from policy dependent human feedback"
    - "overcoming exploration in reinforcement learning with demonstrations"
    - "deep reinforcement learning for robotic manipulation with asynchronous off policy updates"
    - "generalizing skills with semi supervised reinforcement learning"
    - "end to end training of deep visuomotor policies"
    - "playing atari with deep reinforcement learning"
    - "hierarchical reinforcement learning with the maxq value function decomposition"

Connected through hyperedge HE-3 (induced by source paper "energy efficient hadamard neural networks"):
  with members:
    - "compressed video action recognition"
    - "energy saving additive neural network"
    - "bitwise neural networks"
    - "learning both weights and connections for efficient neural networks"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "striving for simplicity the all convolutional net"

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)