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: "architecture search of dynamic cells for semantic video segmentation"
  Abstract: In semantic video segmentation the goal is to acquire consistent dense semantic labelling across image frames. To this end, recent approaches have been reliant on manually arranged operations applied on top of static semantic segmentation networks - with the most prominent building block being the optical flow able to provide information about scene dynamics. Related to that is the line of research concerned with speeding up static networks by approximating expensive parts of them with cheaper alternatives, while propagating information from previous frames. In this work we attempt to come up with generalisation of those methods, and instead of manually designing contextual blocks that connect per-frame outputs, we propose a neural architecture search solution, where the choice of operations together with their sequential arrangement are being predicted by a separate neural network. We showcase that such generalisation leads to stable and accurate results across common benchmarks, such as CityScapes and CamVid datasets. Importantly, the proposed methodology takes only 2 GPU-days, finds high-performing cells and does not rely on the expensive optical flow computation.

Connected through hyperedge HE-1 (induced by source paper "dynamic face video segmentation via reinforcement learning"):
  with members:
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "face mask extraction in video sequence"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "meta learning transferable active learning policies by deep reinforcement learning"
    - "dynamic video segmentation network"
    - "low latency video semantic segmentation"
    - "light weight refinenet for real time semantic segmentation"
    - "inter bmv interpolation with block motion vectors for fast semantic segmentation on video"

Connected through hyperedge HE-2 (induced by source paper "device circuit architecture co exploration for computing in memory neural accelerators"):
  with members:
    - "video action recognition via neural architecture searching"
    - "hardware software co exploration of neural architectures"
    - "accuracy vs efficiency achieving both through fpga implementation aware neural architecture search"
    - "achieving super linear speedup across multi fpga for real time dnn inference"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "on neural architecture search for resource constrained hardware platforms"
    - "fbnet hardware aware efficient convnet design via differentiable neural architecture search"
    - "yolov3 an incremental improvement"

Connected through hyperedge HE-3 (induced by source paper "hardware software co exploration of neural architectures"):
  with members:
    - "design flow of accelerating hybrid extremely low bit width neural network in embedded fpga"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "haq hardware aware automated quantization with mixed precision"
    - "darts differentiable architecture search"
    - "horovod fast and easy distributed deep learning in tensorflow"
    - "mnasnet platform aware neural architecture search for mobile"
    - "large scale evolution of image classifiers"

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)