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: "surgical data science for next generation interventions"
  Abstract: Interventional healthcare will evolve from an artisanal craft based on the individual experiences, preferences and traditions of physicians into a discipline that relies on objective decision-making on the basis of large-scale data from heterogeneous sources.

Connected through hyperedge HE-1 (induced by source paper "multi task recurrent neural network for surgical gesture recognition and progress prediction"):
  with members:
    - "using 3d convolutional neural networks to learn spatiotemporal features for automatic surgical gesture recognition in video"
    - "weakly supervised recognition of surgical gestures"
    - "deep reinforcement learning for surgical gesture segmentation and classification"
    - "end to end fine grained action segmentation and recognition using conditional random field models and discriminative sparse coding"
    - "joint surgical gesture and task classification with multi task and multimodal learning"
    - "less is more surgical phase recognition with less annotations through self supervised pre training of cnn lstm networks"
    - "unsupervised temporal context learning using convolutional neural networks for laparoscopic workflow analysis"
    - "tricornet a hybrid temporal convolutional and recurrent network for video action segmentation"

Connected through hyperedge HE-2 (induced by source paper "cadis cataract dataset for image segmentation"):
  with members:
    - "2017 robotic instrument segmentation challenge"
    - "improving surgical training phantoms by hyperrealism deep unpaired image to image translation from real surgeries"
    - "unified perceptual parsing for scene understanding"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "deepphase surgical phase recognition in cataracts videos"
    - "rethinking atrous convolution for semantic image segmentation"
    - "endonet a deep architecture for recognition tasks on laparoscopic videos"
    - "xception deep learning with depthwise separable convolutions"

Connected through hyperedge HE-3 (induced by source paper "accurate and interpretable evaluation of surgical skills from kinematic data using fully convolutional neural networks"):
  with members:
    - "deep learning for time series classification a review"
    - "transfer learning for time series classification"
    - "deep learning with convolutional neural network for objective skill evaluation in robot assisted surgery"
    - "evaluating surgical skills from kinematic data using convolutional neural networks"
    - "automated surgical skill assessment in rmis training"
    - "time series classification from scratch with deep neural networks a strong baseline"
    - "learning deep features for discriminative localization"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "generating large labeled data sets for laparoscopic image processing tasks using unpaired image to image translation"):
  with members:
    - "multimodal unsupervised image to image translation"
    - "ternausnet u net with vgg11 encoder pre trained on imagenet for image segmentation"
    - "gantruth an unpaired image to image translation method for driving scenarios"
    - "spigan privileged adversarial learning from simulation"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "cyclegan a master of steganography"
    - "endonet a deep architecture for recognition tasks on laparoscopic videos"

Connected through hyperedge HE-5 (induced by source paper "face detection in the operating room comparison of state of the art methods and a self supervised approach"):
  with members:
    - "simple baselines for human pose estimation and tracking"
    - "faceoff anonymizing videos in the operating rooms"
    - "mvor a multi view rgb d operating room dataset for 2d and 3d human pose estimation"
    - "s 3 fd single shot scale invariant face detector"
    - "data distillation towards omni supervised learning"
    - "articulated clinician detection using 3d pictorial structures on rgb d data"
    - "ssh single stage headless face detector"
    - "cascaded pyramid network for multi person pose estimation"

Connected through hyperedge HE-6 (induced by source paper "faceoff anonymizing videos in the operating rooms"):
  with members:
    - "cms rcnn contextual multi scale region based cnn for unconstrained face detection"
    - "face detection with the faster r cnn"
    - "hyperface a deep multi task learning framework for face detection landmark localization pose estimation and gender recognition"
    - "multi view face detection using deep convolutional neural networks"
    - "fast r cnn"
    - "wider face a face detection benchmark"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "deep learning face attributes in the wild"

Connected through hyperedge HE-7 (induced by source paper "deepphase surgical phase recognition in cataracts videos"):
  with members:
    - "endonet a deep architecture for recognition tasks on laparoscopic videos"
    - "the tum lapchole dataset for the m2cai 2016 workflow challenge"
    - "temporal convolutional networks a unified approach to action segmentation"
    - "deep residual learning for image recognition"
    - "imagenet large scale visual recognition challenge"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-8 (induced by source paper "evaluating surgical skills from kinematic data using convolutional neural networks"):
  with members:
    - "automated surgical skill assessment in rmis training"
    - "time series classification from scratch with deep neural networks a strong baseline"
    - "learning deep features for discriminative localization"
    - "adam a method for stochastic optimization"

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.CY(Computers and Society)